Triple
T3112712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winaray |
E64986
|
entity |
| Predicate | spokenIn |
P2266
|
FINISHED |
| Object | Biliran |
E261520
|
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: Biliran | Statement: [Winaray, spokenIn, Biliran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biliran Context triple: [Winaray, spokenIn, Biliran]
-
A.
Biliran
chosen
Biliran is an island province in the central Philippines known for its volcanic landscapes, waterfalls, and coastal scenery.
-
B.
Sibulan
Sibulan is a coastal municipality in the Philippine province of Negros Oriental known as a gateway to Dumaguete City and for its local airport and seaport.
-
C.
Bislig
Bislig is a coastal city in the Caraga region of Mindanao in the Philippines, known for its proximity to the Tinuy-an Falls and its history as a former major paper-mill town.
-
D.
Balanga
Balanga is a coastal city in the province of Bataan in the Philippines, situated along the shores of Manila Bay.
-
E.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
- 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_69ad857eeaf48190b34ebfdaa7a264cf |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada43b0b3c8190a828c9cfcf730ed9 |
completed | March 8, 2026, 4:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20397cbdc8190abdd93b502f9577f |
completed | March 12, 2026, 12:06 a.m. |
Created at: March 8, 2026, 3:04 p.m.