Triple
T11062075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madridejos |
E261530
|
entity |
| Predicate | sharesIslandWith |
P5790
|
FINISHED |
| Object | Bantayan |
E116248
|
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: Bantayan | Statement: [Madridejos, sharesIslandWith, Bantayan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bantayan Context triple: [Madridejos, sharesIslandWith, Bantayan]
-
A.
Bantayan Island
chosen
Bantayan Island is a scenic island in the central Philippines known for its white-sand beaches, clear waters, and laid-back coastal villages.
-
B.
Bantayan town
Bantayan town is a coastal municipality in the province of Cebu, Philippines, serving as one of the main population and commercial centers on Bantayan Island.
-
C.
Itbayat Island
Itbayat Island is the northernmost and largest inhabited island of the Batanes group in the Philippines, known for its rugged cliffs, traditional stone houses, and exposure to strong typhoons.
-
D.
Bauan
Bauan is a coastal municipality in the province of Batangas in the Philippines, known for its diving spots, marine sanctuaries, and industrial facilities.
-
E.
Siargao Island
Siargao Island is a renowned Philippine island destination famous for its world-class surfing waves, pristine beaches, and laid-back tropical atmosphere.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798eb838c819089a89c55209c0295 |
completed | April 9, 2026, 12:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6849dbff08190a352eaaea8606bdb |
completed | April 20, 2026, 7:55 p.m. |
Created at: April 8, 2026, 9:26 p.m.