Triple
T21831714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valle Department |
E539011
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Amapala |
—
|
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: Amapala | Statement: [Valle Department, hasMunicipality, Amapala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amapala Context triple: [Valle Department, hasMunicipality, Amapala]
-
A.
Amapala
chosen
Amapala is a coastal town and former major Pacific port of Honduras located on El Tigre Island in the Gulf of Fonseca.
-
B.
Pacasmayo
Pacasmayo is a coastal city in northern Peru known for its long pier, surfing beaches, and colonial-era architecture.
-
C.
Achacachi
Achacachi is a town in Bolivia known as a commercial and cultural center of the Aymara people near Lake Titicaca.
-
D.
Tumbalá
Tumbalá is a municipality in the Mexican state of Chiapas, known for its lush jungle landscapes and proximity to popular natural attractions.
-
E.
Sipacapa
Sipacapa is a highland municipality in the San Marcos department of western Guatemala, known for its predominantly Sipakapense Maya population and traditional indigenous culture.
- 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_69e0c475cda88190987d08f23caebdc1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f091362d9081909f00ad7a2806d5cb |
completed | April 28, 2026, 10:51 a.m. |
Created at: April 16, 2026, 6:55 p.m.