Triple
T14760126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sertãozinho |
E346833
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object | Jaboticabal |
E1042874
|
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: Jaboticabal | Statement: [Sertãozinho, hasNeighbour, Jaboticabal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jaboticabal Context triple: [Sertãozinho, hasNeighbour, Jaboticabal]
-
A.
Jaboticabal
chosen
Jaboticabal is a municipality in the state of São Paulo, Brazil, known for its strong agricultural economy and educational institutions.
-
B.
Guaratinguetá
Guaratinguetá is a historic municipality in southeastern Brazil known for its colonial heritage and religious tourism, located in the state of São Paulo.
-
C.
Duas Barras
Duas Barras is a small municipality in the mountainous interior of Rio de Janeiro state in southeastern Brazil.
-
D.
Uberaba
Uberaba is a mid-sized Brazilian city in the western part of Minas Gerais state, known for its strong agribusiness sector and cattle breeding traditions.
-
E.
Itapetininga
Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
- 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_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_69ff56afa5ec8190a058574dff7431dc |
completed | May 9, 2026, 3:45 p.m. |
Created at: April 10, 2026, 1:30 a.m.