Triple
T9910683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Catalonia |
E185132
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Manresa |
E186358
|
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: Manresa | Statement: [Central Catalonia, capital, Manresa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manresa Context triple: [Central Catalonia, capital, Manresa]
-
A.
Manresa
chosen
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
-
B.
Begur
Begur is a picturesque coastal town in Catalonia, Spain, known for its medieval hilltop castle, charming old quarter, and scenic beaches along the Costa Brava.
-
C.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
D.
Empuriabrava
Empuriabrava is a large seaside resort on Spain’s Costa Brava, famous for its extensive network of navigable canals and marina-style residential development.
-
E.
Cerdanya
Cerdanya is a historic region in the eastern Pyrenees, now divided between France and Spain, known for its mountainous landscapes and Catalan cultural heritage.
- 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_69ca8296165881908ca4750701af1f29 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb512a26881908eb72a21ffb1efef |
completed | April 2, 2026, 12:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d74fb63b7081909cb6faddd795ced6 |
completed | April 9, 2026, 7:05 a.m. |
Created at: March 30, 2026, 8:41 p.m.