Triple
T9976329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loir-et-Cher |
E196339
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Vendôme |
E104780
|
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: Vendôme | Statement: [Loir-et-Cher, contains, Vendôme]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vendôme Context triple: [Loir-et-Cher, contains, Vendôme]
-
A.
Vendôme
chosen
Vendôme is a historic town in the Loir-et-Cher department of central France, known for its medieval architecture and role as a former stronghold of the counts and dukes of Vendôme.
-
B.
Bourbon-Vendôme
Bourbon-Vendôme was a cadet branch of the French royal House of Bourbon that produced several prominent nobles and ultimately the first Bourbon king of France, Henry IV.
-
C.
Gave de Pau
Gave de Pau is a river in southwestern France that flows through the city of Pau and forms part of the Adour river system in the Pyrenees region.
-
D.
Cambronne
Cambronne is a Paris Métro station located in the 15th arrondissement, named after the French general Pierre Cambronne.
-
E.
Cazeneuve
Cazeneuve is a French surname most notably borne by Bernard Cazeneuve, a prominent French politician and former Prime Minister of France.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb84b47308190aa2f94fa7320cdc3 |
completed | April 2, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23de601f0819096004bf60ffa2d2c |
completed | April 5, 2026, 10:48 a.m. |
Created at: March 30, 2026, 8:48 p.m.