Triple
T856478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stéphane Mille |
E18502
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Stéphane
Stéphane is a French masculine given name, equivalent to Stephen in English, commonly used in Francophone countries.
|
E132761
|
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: Stéphane | Statement: [Stéphane Mille, givenName, Stéphane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stéphane Context triple: [Stéphane Mille, givenName, Stéphane]
-
A.
Jérôme
Jérôme is a masculine given name of French origin, famously borne by Jérôme Bonaparte, the youngest brother of Napoleon I.
-
B.
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.
-
C.
Thibault
Thibault is a surname most notably associated with Mike Thibault, a prominent American basketball coach in the WNBA.
-
D.
Pierre
Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
-
E.
Laurent
Laurent is a Belgian prince, the younger son of King Albert II and Queen Paola, known for his environmental interests and occasional public controversies.
- 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: Stéphane Triple: [Stéphane Mille, givenName, Stéphane]
Generated description
Stéphane is a French masculine given name, equivalent to Stephen in English, commonly used in Francophone countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stéphane Target entity description: Stéphane is a French masculine given name, equivalent to Stephen in English, commonly used in Francophone countries.
-
A.
Jérôme
Jérôme is a masculine given name of French origin, famously borne by Jérôme Bonaparte, the youngest brother of Napoleon I.
-
B.
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.
-
C.
Thibault
Thibault is a surname most notably associated with Mike Thibault, a prominent American basketball coach in the WNBA.
-
D.
Pierre
Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
-
E.
Laurent
Laurent is a Belgian prince, the younger son of King Albert II and Queen Paola, known for his environmental interests and occasional public controversies.
- 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac3c172481908ed164ee1579ec28 |
completed | March 1, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac5e95e25881909c3e167417c0ae47 |
completed | March 7, 2026, 5:21 p.m. |
| NEDg | Description generation | batch_69ac5f15c2808190905e40c6db9d957c |
completed | March 7, 2026, 5:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac633749008190bae63644d5ee7cea |
completed | March 7, 2026, 5:41 p.m. |
Created at: March 1, 2026, 7:39 p.m.