Triple
T6662305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campo Grande |
E151505
|
entity |
| Predicate | hasLegislature |
P239
|
FINISHED |
| Object | Municipal Chamber of Campo Grande |
E151505
|
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: Municipal Chamber of Campo Grande | Statement: [Campo Grande, hasLegislature, Municipal Chamber of Campo Grande]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Municipal Chamber of Campo Grande Context triple: [Campo Grande, hasLegislature, Municipal Chamber of Campo Grande]
-
A.
Campo Grande
Campo Grande is a neighborhood in the city of Recife, Brazil.
-
B.
Campo Grande
chosen
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.
-
C.
Campo Grande
Campo Grande is a major transport hub in Lisbon that serves as a key connection point for metro, bus, and other public transit services.
-
D.
Três Lagoas
Três Lagoas is a Brazilian city in the state of Mato Grosso do Sul known for its strong pulp and paper industry and growing industrial sector.
-
E.
Municipal government of Cuiabá
The Municipal government of Cuiabá is the local public administration responsible for managing city services, urban planning, and municipal policies in the capital of Brazil’s Mato Grosso state.
- 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_69c687f5fac48190a09e4838d9c6b45d |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b097e0e481909251443f9ce0b85a |
completed | March 27, 2026, 4:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6ef0a0c208190ac6a309bfb2e4e4b |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 2:02 p.m.