Triple
T12910337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surigao City |
E308843
|
entity |
| Predicate | language |
P15
|
FINISHED |
| Object | Surigaonon |
E243832
|
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: Surigaonon | Statement: [Surigao City, language, Surigaonon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Surigaonon Context triple: [Surigao City, language, Surigaonon]
-
A.
Surigaonon
chosen
Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
-
B.
Nasugbu
Nasugbu is a coastal municipality in the province of Batangas, Philippines, known for its beaches, resorts, and agricultural areas.
-
C.
Nabunturan
Nabunturan is a landlocked municipality in the Philippines known as the administrative and commercial center of the province of Davao de Oro on Mindanao island.
-
D.
Canlaon
Canlaon is a city in the Philippines known for its proximity to Mount Kanlaon, an active volcano and prominent natural landmark on Negros Island.
-
E.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9719e584c81909be1ac1366effca0 |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eacc2f808190bcc6817b7ce82750 |
completed | May 3, 2026, 6:27 a.m. |
Created at: April 9, 2026, 5:41 p.m.