Triple
T15554672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goiás |
E370836
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Anápolis |
E1080124
|
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: Anápolis | Statement: [Goiás, hasCity, Anápolis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anápolis Context triple: [Goiás, hasCity, Anápolis]
-
A.
Anápolis
chosen
Anápolis is a city in the state of Goiás, Brazil, known as an important industrial and logistics hub in the country’s Central-West region.
-
B.
Morada Nova
Morada Nova is a municipality in the state of Ceará in northeastern Brazil, known for its agricultural activities and semi-arid landscape.
-
C.
Sete Lagoas
Sete Lagoas is a city in the state of Minas Gerais, Brazil, known for its industrial activity and automotive manufacturing sector.
-
D.
Brasópolis
Brasópolis is a municipality in the state of Minas Gerais, Brazil, known for its mountainous landscapes and proximity to the Mantiqueira mountain range.
-
E.
Garça
Garça is the Portuguese term for a heron, a long-legged wading bird commonly found near wetlands and waterways.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a96c0c88190808f68601a36b506 |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff456209288190aba6debd434af741 |
completed | May 9, 2026, 2:32 p.m. |
Created at: April 10, 2026, 4:09 a.m.