Triple
T22098997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laguindingan Airport |
E546116
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Villanueva |
—
|
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: Villanueva | Statement: [Laguindingan Airport, cityServed, Villanueva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Villanueva Context triple: [Laguindingan Airport, cityServed, Villanueva]
-
A.
Villanueva
Villanueva is a municipality and town located in the Bolívar Department of northern Colombia.
-
B.
Villanueva
Villanueva is a municipality and town in northwestern Nicaragua, located within the Chinandega Department.
-
C.
Villanueva
chosen
Villanueva is a coastal municipality in the province of Misamis Oriental in the Philippines, known for its industrial facilities and growing local economy.
-
D.
Villanueva
Villanueva is a municipality and town located in the Cortés Department of northwestern Honduras.
-
E.
Villanueva
Villanueva is a municipality in Colombia’s Casanare Department, known for its llanero culture and cattle-ranching traditions on the eastern plains.
- 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_69e11e36d03c8190a83a1ba802b7231b |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129131b4c8190b443bc820d9b5c61 |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.