Triple
T10897201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarangani |
E257340
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Alabel |
E288639
|
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: Alabel | Statement: [Sarangani, hasMunicipality, Alabel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alabel Context triple: [Sarangani, hasMunicipality, Alabel]
-
A.
Alabel
chosen
Alabel is a municipality in the Philippines known as the capital town of the province of Sarangani in the Soccsksargen region of Mindanao.
-
B.
Kalabo
Kalabo is a significant town in western Zambia’s Barotseland region, serving as a local administrative and commercial center near the Zambezi floodplains.
-
C.
Abyek
Abyek is a city in Iran known as one of the major urban centers of Qazvin Province.
-
D.
Alberg
Alberg is a surname most notably associated with Tom Alberg, an American lawyer, venture capitalist, and early Amazon investor.
-
E.
Lezo
Lezo is a small inland municipality in the province of Aklan in the Philippines, known for its rural character and local agricultural 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75d02e4c88190b8286078e90bf913 |
completed | April 9, 2026, 8:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e216b417bc8190b35477e9d363a289 |
completed | April 17, 2026, 11:17 a.m. |
Created at: April 8, 2026, 9:21 p.m.