Triple
T2720424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of São Paulo |
E60066
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Pedreira
Pedreira is a municipality in the state of São Paulo, Brazil, known for its ceramics industry and decorative household goods.
|
E294684
|
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: Pedreira | Statement: [State of São Paulo, hasCity, Pedreira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pedreira Context triple: [State of São Paulo, hasCity, Pedreira]
-
A.
Capileira
Capileira is a picturesque mountain village in Spain’s Alpujarras region, known for its traditional whitewashed houses and dramatic location on the southern slopes of the Sierra Nevada.
-
B.
Cabaceiras
Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
-
C.
Espinheiro
Espinheiro is a central neighborhood in Recife, Brazil, known for its residential areas, commerce, and urban amenities.
-
D.
Areias
Areias is a neighborhood within the city of Recife, Brazil, known as part of its urban residential area.
-
E.
Taboão da Serra
Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
- 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: Pedreira Triple: [State of São Paulo, hasCity, Pedreira]
Generated description
Pedreira is a municipality in the state of São Paulo, Brazil, known for its ceramics industry and decorative household goods.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pedreira Target entity description: Pedreira is a municipality in the state of São Paulo, Brazil, known for its ceramics industry and decorative household goods.
-
A.
Capileira
Capileira is a picturesque mountain village in Spain’s Alpujarras region, known for its traditional whitewashed houses and dramatic location on the southern slopes of the Sierra Nevada.
-
B.
Cabaceiras
Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
-
C.
Espinheiro
Espinheiro is a central neighborhood in Recife, Brazil, known for its residential areas, commerce, and urban amenities.
-
D.
Areias
Areias is a neighborhood within the city of Recife, Brazil, known as part of its urban residential area.
-
E.
Taboão da Serra
Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
- 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_69ab4b746d248190958e052045c09255 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdab06d388190acf690787fe58ab5 |
completed | March 7, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbc05d148190b6a0faf10443519d |
completed | March 10, 2026, 6:35 a.m. |
| NEDg | Description generation | batch_69afbc8415388190a39d459ff7a411e4 |
completed | March 10, 2026, 6:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afbcc460b88190986844c39165ef14 |
completed | March 10, 2026, 6:40 a.m. |
Created at: March 6, 2026, 9:55 p.m.