Triple
T11568818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melissa George |
E274328
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Triangle
"Triangle" is a 2009 psychological horror-thriller film known for its mind-bending time-loop narrative and unsettling atmosphere.
|
E933927
|
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: Triangle | Statement: [Melissa George, notableWork, Triangle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Triangle Context triple: [Melissa George, notableWork, Triangle]
-
A.
Triangle
Triangle is a small sugar-producing town in southeastern Zimbabwe known for its large sugar estates and milling operations.
-
B.
Triangle area
The Triangle area is a metropolitan region in North Carolina anchored by the cities of Raleigh, Durham, and Chapel Hill, known for its universities, research institutions, and technology industry.
-
C.
Triangle Distributing
Triangle Distributing is a beverage distribution company known for supplying products such as the gluten-free beer brand Intolerance to retailers.
-
D.
Tregami
Tregami is a Nuristani language spoken by a small community in eastern Afghanistan’s remote valleys.
-
E.
Triangular Field
Triangular Field is a historic battlefield area at Gettysburg, Pennsylvania, known for intense fighting during the American Civil War.
- 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: Triangle Triple: [Melissa George, notableWork, Triangle]
Generated description
"Triangle" is a 2009 psychological horror-thriller film known for its mind-bending time-loop narrative and unsettling atmosphere.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Triangle Target entity description: "Triangle" is a 2009 psychological horror-thriller film known for its mind-bending time-loop narrative and unsettling atmosphere.
-
A.
Triangle
Triangle is a small sugar-producing town in southeastern Zimbabwe known for its large sugar estates and milling operations.
-
B.
Triangle area
The Triangle area is a metropolitan region in North Carolina anchored by the cities of Raleigh, Durham, and Chapel Hill, known for its universities, research institutions, and technology industry.
-
C.
Triangle Distributing
Triangle Distributing is a beverage distribution company known for supplying products such as the gluten-free beer brand Intolerance to retailers.
-
D.
Tregami
Tregami is a Nuristani language spoken by a small community in eastern Afghanistan’s remote valleys.
-
E.
Triangular Field
Triangular Field is a historic battlefield area at Gettysburg, Pennsylvania, known for intense fighting during the American Civil War.
- 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_69d6aae5ac3c81908d2b0a3a665665b2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d88dd543a48190b834abd8e8ae7b65 |
completed | April 10, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6e8dec908819080d97cd33be55b3f |
completed | April 21, 2026, 3:02 a.m. |
| NEDg | Description generation | batch_69e6ef9631e48190aef47bba9ad611e8 |
completed | April 21, 2026, 3:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e6f94ac2d0819098a3024eaab908b5 |
completed | April 21, 2026, 4:12 a.m. |
Created at: April 8, 2026, 9:37 p.m.