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.