Triple
T22488884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esther Ferrer |
E555963
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | San Sebastián |
—
|
NE NERFINISHED |
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: [Esther Ferrer, placeOfBirth, San Sebastián]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Sebastián Context triple: [Esther Ferrer, placeOfBirth, San Sebastián]
-
A.
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.
-
B.
San Sebastián
San Sebastián is a small town located within the Comayagua Department of central Honduras.
-
C.
San Sebastián
San Sebastián is a district and urban area within the San José metropolitan region of Costa Rica, known for its residential neighborhoods and proximity to the country’s capital.
-
D.
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.
-
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 (2 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_69e11e53897c819088863779f8c50bb0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15c3f3b4c819092bfe6495c61db46 |
completed | April 29, 2026, 1:17 a.m. |
Created at: April 16, 2026, 8:49 p.m.