Triple
T19934965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Entrecampos |
E479150
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Campo Grande |
—
|
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: Campo Grande | Statement: [Entrecampos, near, Campo Grande]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Campo Grande Context triple: [Entrecampos, near, Campo Grande]
-
A.
Campo Grande
Campo Grande is a populous residential neighborhood in the western part of Rio de Janeiro, Brazil, known for its commercial centers and suburban character.
-
B.
Campo Grande
Campo Grande is a neighborhood in the city of Recife, Brazil.
-
C.
Campo Grande
Campo Grande is the capital city of Brazil’s Mato Grosso do Sul state and a key urban and transportation hub for visitors heading into the Pantanal wetlands.
-
D.
Campo Grande
chosen
Campo Grande is a major transport hub in Lisbon that serves as a key connection point for metro, bus, and other public transit services.
-
E.
Dourados
Dourados is a major agricultural and commercial city in the Brazilian state of Mato Grosso do Sul, known as an important regional economic and educational center.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a161a6c819084165ea528ec2f64 |
completed | April 20, 2026, 4:53 p.m. |
Created at: April 10, 2026, 1:53 p.m.