Triple
T2124654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cars |
E46398
|
entity |
| Predicate | cinematography |
P1953
|
FINISHED |
| Object | Jean-Claude Kalache |
E243710
|
NE FINISHED |
How this triple was built (2 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: Jean-Claude Kalache | Statement: [Cars, cinematography, Jean-Claude Kalache]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean-Claude Kalache Context triple: [Cars, cinematography, Jean-Claude Kalache]
-
A.
Jean-Claude Kalache
chosen
Jean-Claude Kalache is a cinematographer and lighting artist best known for his work on Pixar animated films such as Monsters, Inc.
-
B.
Alain Mimoun
Alain Mimoun was a French long-distance runner best known for winning the marathon gold medal at the 1956 Melbourne Olympics after years of rivalry with Emil Zátopek.
-
C.
Jean-Claude Olivier
Jean-Claude Olivier is a writer associated with the Juicy brand or publication.
-
D.
Michel Andrault
Michel Andrault was a prominent French architect known for his influential large-scale housing and urban development projects in the late 20th century.
-
E.
Antoine Nahas
Antoine Nahas was a Lebanese architect best known for designing the National Museum of Beirut, a landmark institution of Lebanon’s cultural heritage.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbb55cb2c8190aab8199da3335032 |
completed | March 7, 2026, 5:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1f72ad8c81909da734f6316f747f |
completed | March 9, 2026, 7:28 p.m. |
Created at: March 4, 2026, 7:44 p.m.