Triple
T2615544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aneto |
E58878
|
entity |
| Predicate | nearestTown |
P350
|
FINISHED |
| Object | Benasque |
E282614
|
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: Benasque | Statement: [Aneto, nearestTown, Benasque]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benasque Context triple: [Aneto, nearestTown, Benasque]
-
A.
Benasque Valley
chosen
Benasque Valley is a scenic glacial valley in the central Pyrenees of Spain, renowned for its high mountain landscapes, hiking and skiing, and proximity to the range’s highest peaks.
-
B.
Pau-Ferro
Pau-Ferro is a neighborhood in the city of Recife, Brazil.
-
C.
Manresa
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
-
D.
Garraf
Garraf is a coastal comarca in Catalonia, Spain, known for its Mediterranean landscapes, natural park, and seaside towns such as Sitges and Vilanova i la Geltrú.
-
E.
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.
- 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_69ab4ac444dc819099614e534dd6021f |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8812d808190b794862287a76c16 |
completed | March 7, 2026, 7:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af9088d23c81908927eba84556f90c |
completed | March 10, 2026, 3:31 a.m. |
Created at: March 6, 2026, 9:50 p.m.