Triple
T11486057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Porvenir Airport |
E272280
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Porvenir |
E50872
|
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: Porvenir | Statement: [Porvenir Airport, locatedIn, Porvenir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Porvenir Context triple: [Porvenir Airport, locatedIn, Porvenir]
-
A.
Porvenir
chosen
Porvenir is a small Chilean town on Tierra del Fuego that serves as an important fishing and service port on the Strait of Magellan.
-
B.
Porvenir
Porvenir is a small town that serves as the capital of Pando Department in northern Bolivia, near the border with Brazil.
-
C.
Niquero
Niquero is a coastal town and municipality in Granma Province, Cuba, known for its historical role in the Cuban Revolution and proximity to important coastal and natural areas.
-
D.
Prestea
Prestea is a mining town in southwestern Ghana known for its significant gold deposits and long history of gold extraction.
-
E.
Pacasmayo
Pacasmayo is a coastal city in northern Peru known for its long pier, surfing beaches, and colonial-era architecture.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85a1fc9688190aacc2eed64229b79 |
completed | April 10, 2026, 2:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e624b12c04819085cc5f7dd64283ca |
completed | April 20, 2026, 1:05 p.m. |
Created at: April 8, 2026, 9:36 p.m.