Triple
T2979569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bagac |
E80477
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Balanga |
E254766
|
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: Balanga | Statement: [Bagac, borderedBy, Balanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balanga Context triple: [Bagac, borderedBy, Balanga]
-
A.
Balanga
chosen
Balanga is a coastal city in the province of Bataan in the Philippines, situated along the shores of Manila Bay.
-
B.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
-
C.
Aguiguan
Aguiguan is a small, uninhabited island in the Northern Mariana Islands known for its rugged terrain and seabird colonies.
-
D.
Palimbang
Palimbang is a coastal municipality in the province of Sultan Kudarat in the Philippines, known for its fishing communities and Moro cultural heritage.
-
E.
Malungon
Malungon is a landlocked agricultural municipality in the province of South Cotabato in the Philippines, known for its hilly terrain and farming-based economy.
- 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_69ad8b15f6ac8190be5fd16a33edcb4f |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad999cca40819082e2d6d10bdb7872 |
completed | March 8, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1eede693c8190a49be9d267bed6fa |
completed | March 11, 2026, 10:38 p.m. |
Created at: March 8, 2026, 2:58 p.m.