Triple
T4367723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aurillac |
E98817
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Caucaia |
E157168
|
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: Caucaia | Statement: [Aurillac, twinTown, Caucaia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caucaia Context triple: [Aurillac, twinTown, Caucaia]
-
A.
Caucaia
chosen
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
B.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
C.
Sertãozinho
Sertãozinho is a municipality in the interior of Brazil known for its strong sugarcane-based agribusiness and ethanol production.
-
D.
Pampilhosa da Serra
Pampilhosa da Serra is a small municipality in central Portugal known for its mountainous landscapes, schist villages, and forested river valleys.
-
E.
Igarassu
Igarassu is one of Brazil’s oldest colonial towns, known for its historic churches and coastal location in the northeastern state of Pernambuco.
- 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35201be7081908808e81634060f95 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5dbcec90881908fe83c83119d99fe |
completed | March 14, 2026, 10:06 p.m. |
Created at: March 12, 2026, 11:17 p.m.