Triple
T10054751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UN Security Council Special Notice |
E208831
|
entity |
| Predicate | distinctFrom |
P1612
|
FINISHED |
| Object |
INTERPOL Diffusion
INTERPOL Diffusion is a decentralized alert mechanism used within the INTERPOL network to rapidly share information about wanted persons, threats, or criminal activity among selected member countries.
|
E837035
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: INTERPOL Diffusion | Statement: [UN Security Council Special Notice, distinctFrom, INTERPOL Diffusion]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: INTERPOL Diffusion Context triple: [UN Security Council Special Notice, distinctFrom, INTERPOL Diffusion]
-
A.
DALL·E
DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
-
B.
CycleGAN
CycleGAN is a type of generative adversarial network designed for unpaired image-to-image translation, enabling conversion between visual domains without requiring matched training examples.
-
C.
StyleGAN
StyleGAN is a state-of-the-art generative adversarial network architecture known for producing highly realistic, controllable images by manipulating disentangled style representations at different layers of the network.
-
D.
PixelRNN
PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
-
E.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: INTERPOL Diffusion Triple: [UN Security Council Special Notice, distinctFrom, INTERPOL Diffusion]
Generated description
INTERPOL Diffusion is a decentralized alert mechanism used within the INTERPOL network to rapidly share information about wanted persons, threats, or criminal activity among selected member countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: INTERPOL Diffusion Target entity description: INTERPOL Diffusion is a decentralized alert mechanism used within the INTERPOL network to rapidly share information about wanted persons, threats, or criminal activity among selected member countries.
-
A.
DALL·E
DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
-
B.
CycleGAN
CycleGAN is a type of generative adversarial network designed for unpaired image-to-image translation, enabling conversion between visual domains without requiring matched training examples.
-
C.
StyleGAN
StyleGAN is a state-of-the-art generative adversarial network architecture known for producing highly realistic, controllable images by manipulating disentangled style representations at different layers of the network.
-
D.
PixelRNN
PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
-
E.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca836094408190a36a1ea7e9a86fcd |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcfab39408190ac728fe156eed658 |
completed | April 2, 2026, 2:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d282a3d3148190908f02a9588b700e |
completed | April 5, 2026, 3:41 p.m. |
| NEDg | Description generation | batch_69d2839311508190a59c1d393cc38785 |
completed | April 5, 2026, 3:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2843fff188190a21ced57523c329a |
completed | April 5, 2026, 3:48 p.m. |
Created at: March 30, 2026, 8:57 p.m.