Triple
T4429387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Cebu |
E95287
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Dalaguete |
E374780
|
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: Dalaguete | Statement: [Province of Cebu, hasMunicipality, Dalaguete]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dalaguete Context triple: [Province of Cebu, hasMunicipality, Dalaguete]
-
A.
Dalaguete
chosen
Dalaguete is a coastal municipality in the province of Cebu in the Philippines, known for its cool highland areas and vegetable farming.
-
B.
Danao City
Danao City is a component city in the province of Cebu in the Philippines, known historically for its gun-making industry and as a growing commercial and industrial hub in the region.
-
C.
Argao
Argao is a coastal municipality in the southeastern part of Cebu, Philippines, known for its Spanish-era heritage structures and traditional delicacies.
-
D.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
-
E.
Moalboal
Moalboal is a coastal town in the Philippines renowned for its vibrant coral reefs, sardine runs, and popular diving and snorkeling spots.
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35568767c819084d5e18b56a4745e |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf7fbd0bc881908ba07edb75479b97 |
completed | March 22, 2026, 5:35 a.m. |
Created at: March 12, 2026, 11:30 p.m.