Triple
T3544893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takasaki |
E74971
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object | Sao Jose dos Campos |
E210016
|
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: Sao Jose dos Campos | Statement: [Takasaki, hasSisterCity, Sao Jose dos Campos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sao Jose dos Campos Context triple: [Takasaki, hasSisterCity, Sao Jose dos Campos]
-
A.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
B.
Campinas
Campinas is a major city in the state of São Paulo, Brazil, known as an important industrial, technological, and transportation hub in the country.
-
C.
São José dos Campos, Brazil
chosen
São José dos Campos, Brazil is a major industrial and technological hub in the state of São Paulo, known especially for its aerospace industry and research institutions.
-
D.
Piracicaba
Piracicaba is a city in the state of São Paulo, Brazil, known for its strong agricultural and industrial economy and as a regional educational center.
-
E.
Barueri
Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
- 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_69ad85d274cc8190ab59c97298a1cfbf |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbf76c5b08190b898d31b80a3a350 |
completed | March 8, 2026, 6:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f01c47d881908e9489db7bf47b11 |
completed | March 14, 2026, 5:20 a.m. |
Created at: March 8, 2026, 3:20 p.m.