Triple
T14098772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Iraya |
E339323
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Basco |
E591119
|
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: Basco | Statement: [Mount Iraya, near, Basco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Basco Context triple: [Mount Iraya, near, Basco]
-
A.
Basco
chosen
Basco is a small coastal town in the northern Philippines known as the administrative and cultural center of the remote, scenic Batanes island province.
-
B.
Puerto Marqués
Puerto Marqués is a coastal bay and beach community near Acapulco in the Mexican state of Guerrero, known for its calm waters and tourism.
-
C.
Jarabacoa
Jarabacoa is a mountainous town in the Dominican Republic known for its cool climate, rivers, and outdoor adventure tourism.
-
D.
Puerto Coloso
Puerto Coloso is a coastal port facility in northern Chile that serves as the main export terminal for copper concentrate from the Escondida mine.
-
E.
Puerto Casado
Puerto Casado is a small river port town in northern Paraguay known historically for its tannin industry and its strategic location on the Paraguay River.
- 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_69d81c69b5c8819094aa1abf18302908 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5fba7c10819095b1299b7b4f0310 |
completed | April 14, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd193332308190870c4ec7c9753202 |
completed | May 7, 2026, 10:58 p.m. |
Created at: April 9, 2026, 10:22 p.m.