Triple
T6891769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biscay |
E159064
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Basauri |
E562127
|
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: Basauri | Statement: [Biscay, contains, Basauri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Basauri Context triple: [Biscay, contains, Basauri]
-
A.
Basauri
chosen
Basauri is a town and municipality in the province of Biscay in Spain’s Basque Country, forming part of the Greater Bilbao metropolitan area.
-
B.
Belbali
Belbali is an alternative name for the Korandje language, a minority Zenati Berber language spoken in the oasis of Tabelbala in southwestern Algeria.
-
C.
Ozurgeti
Ozurgeti is a city in western Georgia that serves as the administrative and cultural center of the Guria region.
-
D.
Santanyí
Santanyí is a picturesque coastal town in southeastern Mallorca, Spain, known for its traditional stone architecture, weekly markets, and nearby sandy coves.
-
E.
Getxo
Getxo is a coastal town in the Basque Country of northern Spain, known for its beaches, historic neighborhoods, and proximity to Bilbao.
- 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d92ecbdc8190992f9c7f4f33f4c4 |
completed | March 27, 2026, 7:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7511fbe808190bc3dfb7c34a7cbb6 |
completed | March 28, 2026, 3:55 a.m. |
Created at: March 27, 2026, 2:24 p.m.