Triple
T22301111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maria Mauban |
E551257
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Mauban |
—
|
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: Mauban | Statement: [Maria Mauban, familyName, Mauban]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mauban Context triple: [Maria Mauban, familyName, Mauban]
-
A.
Mauban
chosen
Mauban is a coastal municipality in the province of Quezon, Philippines, known for its port, fishing industry, and access to nearby islands and natural attractions.
-
B.
Narvacan
Narvacan is a coastal municipality in the province of Ilocos Sur in the Philippines, known for its historic churches, scenic beaches, and wind-swept landscapes.
-
C.
Koronadal
Koronadal is a city in the Philippines that serves as the capital of South Cotabato and the regional administrative center of Soccsksargen.
-
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.
Malapatan
Malapatan is a coastal municipality in the province of Sarangani in the Philippines, known for its diverse indigenous communities and agricultural economy.
- 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_69e11e46c0188190800181a4233f28fe |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f157245ee481909a7a3397de3ae355 |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.