Triple
T21150966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deux-Sèvres |
E521185
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Thouars |
—
|
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: Thouars | Statement: [Deux-Sèvres, contains, Thouars]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thouars Context triple: [Deux-Sèvres, contains, Thouars]
-
A.
Thouars
chosen
Thouars is a historic town in western France known for its medieval architecture and strategic position overlooking the Thouet River.
-
B.
Calomarde
Calomarde is a small municipality in the province of Teruel, Aragon, Spain, situated within the mountainous Sierra de Albarracín region.
-
C.
Loudun
Loudun is a historic town in western France’s Vienne department, known for its medieval architecture and its association with the 17th-century Loudun possessions and witch trials.
-
D.
Yssingeaux
Yssingeaux is a commune in south-central France that serves as an administrative and service center in the Haute-Loire department.
-
E.
Saint-Mard
Saint-Mard is a French commune in the Seine-et-Marne department in the Île-de-France region, northeast of Paris.
- 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_69e0b50c6a848190a4e525a77a319b8a |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72401830c8190a2008c40c4174d97 |
completed | April 21, 2026, 7:15 a.m. |
Created at: April 16, 2026, 2:58 p.m.