Triple
T9553952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Boulou |
E230493
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Le Boulou |
E475625
|
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: Le Boulou | Statement: [Battle of Boulou, location, Le Boulou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Boulou Context triple: [Battle of Boulou, location, Le Boulou]
-
A.
Le Boulou
chosen
Le Boulou is a small spa town in the Vallespir region of southern France, known for its thermal springs and proximity to the Pyrenees and the Spanish border.
-
B.
Boulou
Boulou is an alternate name for the Bulu language, a Bantu language spoken primarily in Cameroon.
-
C.
Luchon
Luchon is a renowned spa and mountain resort town in the French Pyrenees, famous for its thermal baths and outdoor activities.
-
D.
Marvejols
Marvejols is a historic town in southern France’s Lozère department, known for its medieval heritage and location near the Aubrac and Margeride regions.
-
E.
Montauban
Montauban is a historic city in southern France known for its red-brick architecture and role as the capital of the Tarn-et-Garonne department.
- 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_69ca847d3be8819099c9dad2a7e786f1 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99217de48190b528e14fd02ee987 |
completed | April 1, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1528bd99881909e3f51472a99917f |
completed | April 4, 2026, 6:03 p.m. |
Created at: March 30, 2026, 8:02 p.m.