Triple
T17215530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fernando Savater |
E417842
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | San Sebastián |
E138087
|
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: San Sebastián | Statement: [Fernando Savater, placeOfBirth, San Sebastián]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Sebastián Context triple: [Fernando Savater, placeOfBirth, San Sebastián]
-
A.
San Sebastián
San Sebastián is a small town located within the Comayagua Department of central Honduras.
-
B.
San Sebastián
San Sebastián is a Guatemalan town located in the highlands of the San Marcos department, known for its proximity to Central America’s highest peak, Volcán Tajumulco.
-
C.
Donostia-San Sebastián
chosen
Donostia-San Sebastián is a coastal city in Spain’s Basque Country renowned for its picturesque bay, beaches, and world-class gastronomy.
-
D.
Bilbao
Bilbao is a major port city in northern Spain renowned for its industrial heritage, cultural institutions like the Guggenheim Museum, and role as an economic hub of the Basque Country.
-
E.
Bilbao
Bilbao is a station on Madrid's Metro network, serving Line 1 and located in the central Chamberí district.
- 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_69d886d779488190b131369541c04e7d |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42dc9f96881909eb86786a76e17e4 |
completed | April 19, 2026, 1:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170eb9954819085e8c078cf137dc5 |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:38 a.m.