Triple
T3555386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ariège |
E75206
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Foix |
E92446
|
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: Foix | Statement: [Ariège, passesThrough, Foix]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Foix Context triple: [Ariège, passesThrough, Foix]
-
A.
Foix
chosen
Foix is a historic town in southwestern France known for its medieval castle and role as the former capital of the County of Foix.
-
B.
Fuissé
Fuissé is a renowned wine-producing village in France’s Burgundy region, particularly famous for its Pouilly-Fuissé Chardonnay wines.
-
C.
Faya-Largeau
Faya-Largeau is the largest oasis town in northern Chad and an important administrative and trade center in the Sahara Desert.
-
D.
Ferreries
Ferreries is a small inland town and municipality on the Spanish Balearic island of Menorca, known for its traditional crafts and rural surroundings.
-
E.
Louvois
Louvois was a powerful French statesman, best known as Louis XIV’s influential war minister who significantly shaped France’s military administration in the late 17th century.
- 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_69ad85d45090819086f34fb85d850a1e |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0569fbc81909b855b6990c1415b |
completed | March 8, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b38bf1c3e881908e7fc3b4df24e72b |
completed | March 13, 2026, 4 a.m. |
Created at: March 8, 2026, 3:20 p.m.