Triple
T6632196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piauí |
E149952
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Campo Maior
Campo Maior is a municipality in the Brazilian state of Piauí, known historically for its role in regional conflicts and its cultural traditions.
|
E613851
|
NE FINISHED |
How this triple was built (4 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 Maior | Statement: [Piauí, hasCity, Campo Maior]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Campo Maior Context triple: [Piauí, hasCity, Campo Maior]
-
A.
Morrinhos
Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
-
B.
Laranjal Paulista
Laranjal Paulista is a municipality in the state of São Paulo, Brazil, known for its riverside setting and regional agricultural activities.
-
C.
Campo Grande
Campo Grande is a neighborhood in the city of Recife, Brazil.
-
D.
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.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Campo Maior Triple: [Piauí, hasCity, Campo Maior]
Generated description
Campo Maior is a municipality in the Brazilian state of Piauí, known historically for its role in regional conflicts and its cultural traditions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Campo Maior Target entity description: Campo Maior is a municipality in the Brazilian state of Piauí, known historically for its role in regional conflicts and its cultural traditions.
-
A.
Morrinhos
Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
-
B.
Laranjal Paulista
Laranjal Paulista is a municipality in the state of São Paulo, Brazil, known for its riverside setting and regional agricultural activities.
-
C.
Campo Grande
Campo Grande is a neighborhood in the city of Recife, Brazil.
-
D.
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.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6afc9138c81909d228ce4936d6b8b |
completed | March 27, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7006ef73081909fd9081a9184ecd0 |
completed | March 27, 2026, 10:10 p.m. |
| NEDg | Description generation | batch_69c704ad826481909c4b9d4ec18caabc |
completed | March 27, 2026, 10:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c705240f54819094e8715ffd66b352 |
completed | March 27, 2026, 10:31 p.m. |
Created at: March 27, 2026, 1:59 p.m.