Triple
T14760124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sertãozinho |
E346833
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object |
Barrinha
Barrinha is a small municipality in the state of São Paulo, Brazil, known for its agricultural activities and proximity to larger regional centers like Sertãozinho and Ribeirão Preto.
|
E1121050
|
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: Barrinha | Statement: [Sertãozinho, hasNeighbour, Barrinha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barrinha Context triple: [Sertãozinho, hasNeighbour, Barrinha]
-
A.
Bérrio
Bérrio was a Portuguese carrack that served as one of the ships in Vasco da Gama’s pioneering fleet on the first voyage from Portugal to India.
-
B.
Cacilhas
Cacilhas is a riverside district in Almada, Portugal, known for its ferry link to Lisbon and its waterfront restaurants and bars.
-
C.
Barra
Barra is an Arabic female given name historically borne by early Islamic-era women, including relatives of the Prophet Muhammad.
-
D.
Barra
Barra is a scenic island in the Outer Hebrides of Scotland, known for its rugged coastline, Gaelic culture, and the unique beach runway at Barra Airport.
-
E.
Barra
Barra is the surname of Mary Barra, the prominent American business executive and CEO of General Motors.
- 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: Barrinha Triple: [Sertãozinho, hasNeighbour, Barrinha]
Generated description
Barrinha is a small municipality in the state of São Paulo, Brazil, known for its agricultural activities and proximity to larger regional centers like Sertãozinho and Ribeirão Preto.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barrinha Target entity description: Barrinha is a small municipality in the state of São Paulo, Brazil, known for its agricultural activities and proximity to larger regional centers like Sertãozinho and Ribeirão Preto.
-
A.
Bérrio
Bérrio was a Portuguese carrack that served as one of the ships in Vasco da Gama’s pioneering fleet on the first voyage from Portugal to India.
-
B.
Cacilhas
Cacilhas is a riverside district in Almada, Portugal, known for its ferry link to Lisbon and its waterfront restaurants and bars.
-
C.
Barra
Barra is an Arabic female given name historically borne by early Islamic-era women, including relatives of the Prophet Muhammad.
-
D.
Barra
Barra is a scenic island in the Outer Hebrides of Scotland, known for its rugged coastline, Gaelic culture, and the unique beach runway at Barra Airport.
-
E.
Barra
Barra is the surname of Mary Barra, the prominent American business executive and CEO of General Motors.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f207dc819088a53f717736a121 |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe24afff788190ab4925ead7ce90d2 |
completed | May 8, 2026, 6 p.m. |
| NEDg | Description generation | batch_69fe263024e88190b2e838ff772c27c7 |
completed | May 8, 2026, 6:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe267da8c8819094c8e9c8eef3c79c |
completed | May 8, 2026, 6:07 p.m. |
Created at: April 10, 2026, 1:30 a.m.