Triple
T22606740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cuenca province |
E566580
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Tarancón |
—
|
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: Tarancón | Statement: [Cuenca province, hasCity, Tarancón]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarancón Context triple: [Cuenca province, hasCity, Tarancón]
-
A.
Tarancón
chosen
Tarancón is a historic market town and important transport hub in central Spain’s Castilla-La Mancha region.
-
B.
Puertollano
Puertollano is an industrial city in central Spain known for its historical coal mining and energy production industries.
-
C.
Calatañazor
Calatañazor is a small historic village in central Spain known for its medieval architecture and dramatic hilltop setting overlooking the surrounding countryside.
-
D.
Ayamonte
Ayamonte is a Spanish border town in the province of Huelva, Andalusia, situated at the mouth of the Guadiana River opposite Portugal.
-
E.
Caseres
Caseres is a small rural municipality located in the Terra Alta comarca of Catalonia, Spain, known for its agricultural landscape and traditional village character.
- 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_69e245884860819081046ce07d5872c4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1627172488190beb87df965498bcc |
completed | April 29, 2026, 1:44 a.m. |
Created at: April 17, 2026, 2:54 p.m.