Triple
T20570913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michel Ney |
E505093
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Michel |
—
|
NE NERFINISHED |
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: Michel | Statement: [Michel Ney, givenName, Michel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michel Context triple: [Michel Ney, givenName, Michel]
-
A.
Michel
Michel is a fictional character appearing in Frederick Forsyth’s political thriller novel "The Dogs of War."
-
B.
Michel
Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
-
C.
Michel
chosen
Michel is a French given name commonly used for males, equivalent to "Michael" in English.
-
D.
Michel
Michel is the increasingly beleaguered family man in the French psychological thriller "With a Friend Like Harry..." whose life is upended by the obsessive interventions of his old acquaintance Harry.
-
E.
Michel
Michel is a character in Jacques Demy's 1961 French film "Lola," involved in the romantic and emotional entanglements that drive the story.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b721588190993ac7b0a9be2736 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a7a5b0688190b45d0fa993c4765c |
completed | April 20, 2026, 10:24 p.m. |
Created at: April 16, 2026, 11:39 a.m.