Triple
T9732872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region VI |
E235987
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Sipalay |
E298770
|
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: Sipalay | Statement: [Region VI, hasCity, Sipalay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sipalay Context triple: [Region VI, hasCity, Sipalay]
-
A.
Sipalay
chosen
Sipalay is a coastal city in Negros Occidental, Philippines, known for its beaches, diving spots, and laid-back tourism.
-
B.
Bamban
Bamban is a municipality in the province of Tarlac in the Philippines, known for its proximity to Mount Pinatubo and its role in the region’s post-eruption development and eco-tourism.
-
C.
Dipaculao
Dipaculao is a coastal municipality in the Philippine province of Aurora known for its beaches, surfing spots, and scenic mountain landscapes.
-
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.
Sagay
Sagay is a coastal city in the province of Negros Occidental in the Philippines, known for its rich marine resources and protected seascape.
- 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_69ca84d313e88190983ee6ffd0ef60d2 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9eb54fe481908b0202f104b75dc1 |
completed | April 1, 2026, 10:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1afc4dcc4819096d29c1a0529d272 |
completed | April 5, 2026, 12:41 a.m. |
Created at: March 30, 2026, 8:22 p.m.