Triple
T21213332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cartaxo |
E522773
|
entity |
| Predicate | municipalSeat |
P15510
|
FINISHED |
| Object | Cartaxo (town) |
—
|
NE NERFINISHED |
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: Cartaxo (town) | Statement: [Cartaxo, municipalSeat, Cartaxo (town)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cartaxo (town) Context triple: [Cartaxo, municipalSeat, Cartaxo (town)]
-
A.
Cartaxo
chosen
Cartaxo is a Portuguese town in the Ribatejo region known historically for its wine production and agricultural surroundings.
-
B.
Morrinhos
Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
-
C.
São Lourenço da Mata
São Lourenço da Mata is a municipality in the Recife metropolitan region of Pernambuco, Brazil, known for its role in the 2014 FIFA World Cup infrastructure and its mix of urban and forested areas.
-
D.
Indaiatuba
Indaiatuba is a rapidly growing municipality in southeastern Brazil known for its strong industrial base, high quality of life, and proximity to the city of Campinas.
-
E.
Pampilhosa da Serra
Pampilhosa da Serra is a small municipality in central Portugal known for its mountainous landscapes, schist villages, and forested river valleys.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b511ed84819099b449b4a111085c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7347088488190aa764b3f4bbac44d |
completed | April 21, 2026, 8:25 a.m. |
Created at: April 16, 2026, 3:38 p.m.