Triple
T23278430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Castela |
E588786
|
entity |
| Predicate | commonName |
P570
|
FINISHED |
| Object | Castela |
—
|
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: Castela | Statement: [Castela, commonName, Castela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Castela Context triple: [Castela, commonName, Castela]
-
A.
Castela
chosen
Castela is a genus of spiny shrubs and small trees in the quassia family, known for inhabiting arid regions and often possessing bitter chemical compounds.
-
B.
Castilho
Castilho is a Portuguese-language surname associated with various notable figures, including politicians, writers, and public intellectuals.
-
C.
Castilho
Castilho is a municipality in the state of São Paulo, Brazil, located in the western region near the Paraná River and known for agriculture and river-related activities.
-
D.
Guijosa
Guijosa is a small village and administrative subdivision within the municipality of Sigüenza in the province of Guadalajara, Spain.
-
E.
Frasqueira
Frasqueira is a premium category of Madeira wine denoting long-aged, high-quality vintage bottlings.
- 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_69e25d16e2c08190a291de254703129e |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1957991108190ac82fa6dd355f722 |
completed | April 29, 2026, 5:22 a.m. |
Created at: April 17, 2026, 4:49 p.m.