Triple
T15547487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atibaia |
E370647
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Jundiaí |
E318450
|
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: Jundiaí | Statement: [Atibaia, nearbyCity, Jundiaí]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jundiaí Context triple: [Atibaia, nearbyCity, Jundiaí]
-
A.
Jundiaí
chosen
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
-
B.
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.
-
C.
Guarujá
Guarujá is a coastal resort city in southeastern Brazil known for its popular beaches and tourism.
-
D.
Itapetininga
Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
-
E.
Mogi Guaçu
Mogi Guaçu is a municipality in the interior of Brazil’s São Paulo state, known for its industrial activity and the Mogi Guaçu River that runs through it.
- 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a9073948190b6e9cf504aacc7cf |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff875bb0808190a6a4e3b47b524689 |
completed | May 9, 2026, 7:13 p.m. |
Created at: April 10, 2026, 4:08 a.m.