Triple
T14767958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Figueres CF |
E347048
|
entity |
| Predicate | hasHomeCity |
P5864
|
FINISHED |
| Object | Figueres |
—
|
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: Figueres | Statement: [Figueres CF, hasHomeCity, Figueres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Figueres Context triple: [Figueres CF, hasHomeCity, Figueres]
-
A.
Figueres
chosen
Figueres is a town in Catalonia, Spain, best known as the birthplace of surrealist artist Salvador Dalí and home to the Dalí Theatre-Museum.
-
B.
Besalú
Besalú is a well-preserved medieval town in Catalonia, Spain, renowned for its Romanesque architecture and iconic 12th-century stone bridge.
-
C.
Palamós
Palamós is a coastal town and popular tourist destination on Spain’s Costa Brava, known for its fishing port, beaches, and seafood cuisine.
-
D.
Girona
Girona is a historic city in northeastern Catalonia, Spain, known for its well-preserved medieval architecture, walled Old Quarter, and prominent cathedral.
-
E.
Tàrrega
Tàrrega is a historic town in Catalonia, Spain, known for its cultural festivals and medieval heritage.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec81236f081908063bb4350b7b985 |
completed | April 14, 2026, 11:04 p.m. |
Created at: April 10, 2026, 1:30 a.m.