Triple
T3109795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michel Rocard |
E64922
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Michel |
E335552
|
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: Michel | Statement: [Michel Rocard, givenName, Michel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michel Context triple: [Michel Rocard, givenName, Michel]
-
A.
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."
-
B.
Michel
chosen
Michel is a French given name commonly used for males, equivalent to "Michael" in English.
-
C.
Jean-Michel
Jean-Michel is the given name of the influential American artist Jean-Michel Basquiat, a leading figure in 1980s neo-expressionist painting.
-
D.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
E.
René
René is a French given name commonly used for males and historically associated with several notable figures in politics, arts, and philosophy.
- 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_69ad857eeaf48190b34ebfdaa7a264cf |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada2a0ab2481908db50738ec3ad0fb |
completed | March 8, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a4da4b48190987aa1c1f5f61fd9 |
completed | March 12, 2026, 7:55 p.m. |
Created at: March 8, 2026, 3:04 p.m.