Triple
T12915919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean-Marc Perrin |
E308981
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jean-Marc |
E592608
|
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-Marc | Statement: [Jean-Marc Perrin, givenName, Jean-Marc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean-Marc Context triple: [Jean-Marc Perrin, givenName, Jean-Marc]
-
A.
Jean-Marc
chosen
Jean-Marc is a French masculine given name commonly used in Francophone countries.
-
B.
Hervé
Hervé is a French given name, often considered a variant of the English name Harvey, and is commonly used for males in French-speaking regions.
-
C.
Julien BriseBois
Julien BriseBois is a Canadian ice hockey executive best known for building and leading the Tampa Bay Lightning into a modern NHL powerhouse and multiple-time Stanley Cup champion.
-
D.
Stéphane
Stéphane is a French masculine given name, equivalent to Stephen in English, commonly used in Francophone countries.
-
E.
Jean-Marie
Jean-Marie is a French given name most notably borne by Nobel Prize–winning chemist Jean-Marie Lehn.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a1e8088190af697629baecf59f |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a571a3e48190a32d362adc6eaee2 |
completed | May 3, 2026, 1:31 a.m. |
Created at: April 9, 2026, 5:41 p.m.