Triple
T14011607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benguet |
E337091
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Pangasinan |
—
|
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: Pangasinan | Statement: [Benguet, borderedBy, Pangasinan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pangasinan Context triple: [Benguet, borderedBy, Pangasinan]
-
A.
Pangasinan
Pangasinan is an Austronesian language spoken primarily in the Pangasinan province and surrounding areas of northwestern Luzon in the Philippines.
-
B.
Pangasinan
chosen
Pangasinan is a populous coastal province in the Philippines known for its rich Ilocano and Pangasinense culture, agriculture, and tourism sites such as the Hundred Islands National Park.
-
C.
Pampanga
Pampanga is a province in the Central Luzon region of the Philippines, known for its rich culinary heritage, vibrant festivals, and significant role in the country’s history and culture.
-
D.
Sorsogon
Sorsogon is a province in the Bicol Region of the Philippines known for its coastal landscapes, whale shark interactions in Donsol, and rich Bikolano culture.
-
E.
Zambales
Zambales is a coastal province in the Central Luzon region of the Philippines, known for its beaches, mangoes, and ethnolinguistic diversity.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed5cfd0819085b9c860b119a9de |
completed | April 14, 2026, 12:11 p.m. |
Created at: April 9, 2026, 10:19 p.m.