Triple
T16736613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gérard Deschamps |
E406733
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gérard |
E327411
|
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: Gérard | Statement: [Gérard Deschamps, givenName, Gérard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gérard Context triple: [Gérard Deschamps, givenName, Gérard]
-
A.
Gérard
chosen
Gérard is a French given name, equivalent to the Germanic name Gerhard, commonly used in French-speaking 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.
Jean-Pierre
Jean-Pierre is a French given name commonly used as a masculine compound first name.
-
D.
Xavier Fabre
Xavier Fabre is a French architect known for designing prominent cultural venues, including the Mariinsky Concert Hall in Saint Petersburg.
-
E.
Georges Benoît
Georges Benoît was a French cinematographer active in early 20th-century cinema, known for his work on both European and American films.
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e39c3a86848190a03f243dd1bdb899 |
completed | April 18, 2026, 2:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a009d4ea8208190aed0a4014a10d120 |
completed | May 10, 2026, 2:59 p.m. |
Created at: April 10, 2026, 5:20 a.m.