Triple
T6484832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Javier |
E146481
|
entity |
| Predicate | locatedNorthOf |
P305
|
FINISHED |
| Object | Linares |
E98299
|
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: Linares | Statement: [San Javier, locatedNorthOf, Linares]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linares Context triple: [San Javier, locatedNorthOf, Linares]
-
A.
Linares
chosen
Linares is a provincial capital and agricultural city in Chile’s Maule Region, known for its surrounding farmlands and wine production.
-
B.
Linares
Linares is a Spanish football club known for being one of the early teams in the playing career of manager Rafael Benítez.
-
C.
Linares
Linares is a city in the province of Jaén in Andalusia, southern Spain, historically known for its mining industry and cultural heritage.
-
D.
Lucena
Lucena is a historic city in the province of Córdoba, Andalusia, southern Spain, known for its rich cultural heritage and former Jewish community.
-
E.
Lucena
Lucena is a coastal city in the Philippines that serves as the capital and commercial hub of Quezon Province in the Southern Tagalog region.
- 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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a6efe1881909a044b1cdaa511af |
completed | March 22, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c66385943081908630aa0f2cdefa06 |
completed | March 27, 2026, 11:01 a.m. |
Created at: March 22, 2026, 4:52 p.m.