Triple
T23083917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northeast Region |
E575552
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Teresina |
E562759
|
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: Teresina | Statement: [Northeast Region, hasMajorCity, Teresina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teresina Context triple: [Northeast Region, hasMajorCity, Teresina]
-
A.
Teresina
chosen
Teresina is the capital and largest city of the Brazilian state of Piauí, known for its hot climate and location near the confluence of the Parnaíba and Poti rivers.
-
B.
Feira de Santana
Feira de Santana is a major commercial and transportation hub in northeastern Brazil and the second-largest city in the state of Bahia.
-
C.
Goiânia
Goiânia is the capital and largest city of the Brazilian state of Goiás, known as a major regional center for agriculture, industry, and services in central Brazil.
-
D.
São Luís
São Luís is the historic capital of the Brazilian state of Maranhão, known for its well-preserved colonial architecture and rich Afro-Brazilian cultural heritage.
-
E.
São Luís
São Luís is a civil parish in the municipality of Odemira, located in Portugal’s Alentejo region.
- 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_69e245bf3e3c819086d3448720efc01b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18da304548190ab7a421c1ded0eb6 |
completed | April 29, 2026, 4:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0cdf25682081908ff6ee0b898a861a |
completed | May 19, 2026, 10:07 p.m. |
Created at: April 17, 2026, 3:57 p.m.