Triple
T19500226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bacolod–Silay Airport |
E487882
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Talisay |
—
|
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: Talisay | Statement: [Bacolod–Silay Airport, cityServed, Talisay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Talisay Context triple: [Bacolod–Silay Airport, cityServed, Talisay]
-
A.
Talisay
chosen
Talisay is a city in the Philippine province of Negros Occidental known for its sugarcane industry and historical landmarks.
-
B.
Talisay
Talisay is a coastal barangay of the municipality of Daanbantayan in northern Cebu, Philippines.
-
C.
Talisay
Talisay is a coastal municipality in the Philippine province of Camarines Norte known for its rural communities and access to fishing and agricultural resources.
-
D.
Talisay City
Talisay City is a coastal component city in the province of Cebu in the Philippines, known for its historical significance and proximity to Metro Cebu.
-
E.
Calasiao
Calasiao is a municipality in the Philippine province of Pangasinan known for its historic churches and famous native rice cakes called "puto Calasiao."
- 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6350ce7cc819086d77bbd9cd52b53 |
completed | April 20, 2026, 2:15 p.m. |
Created at: April 10, 2026, 1:40 p.m.