Triple
T23123521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simon Pagenaud |
E576962
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Poitiers, France |
—
|
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: Poitiers, France | Statement: [Simon Pagenaud, placeOfBirth, Poitiers, France]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Poitiers, France Context triple: [Simon Pagenaud, placeOfBirth, Poitiers, France]
-
A.
Poitiers
chosen
Poitiers is a historic city in western France known for its Romanesque architecture, medieval heritage, and role as a regional center in the Nouvelle-Aquitaine region.
-
B.
Bourges, France
Bourges, France is a historic city in central France known for its well-preserved medieval architecture and the UNESCO-listed Bourges Cathedral.
-
C.
Blois, France
Blois, France is a historic city on the Loire River known for its Renaissance château and as the birthplace of King Stephen of England.
-
D.
Amiens, France
Amiens, France is a historic city in northern France known for its Gothic cathedral and as the birthplace of French President Emmanuel Macron.
-
E.
Clermont, France
Clermont, France is a historic French town whose name was later adopted by the city of Clermont in Florida, USA.
- 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_69e245f6c2e881909a228fdcfeb7c7d3 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e5299c08190a578102cf2ff7080 |
completed | April 29, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:59 p.m.