Triple
T13150418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Fernando Hill |
E312448
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | San Fernando |
E79508
|
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 Fernando | Statement: [San Fernando Hill, locatedIn, San Fernando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Fernando Context triple: [San Fernando Hill, locatedIn, San Fernando]
-
A.
San Fernando
chosen
San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
-
B.
San Fernando
San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
-
C.
San Fernando
San Fernando is a locality within the municipality of Huixquilucan in the State of Mexico, forming part of the greater Mexico City metropolitan area.
-
D.
San Fernando
San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
-
E.
San Fernando
San Fernando is a coastal municipality in the Philippine province of Masbate, known for its rural communities and fishing-based local economy.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98bd1fc408190b4b5ca973bcee403 |
completed | April 10, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6f5da0f708190b848601e571a9fff |
completed | May 3, 2026, 7:14 a.m. |
Created at: April 9, 2026, 9:11 p.m.