Triple
T9462829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | de Garnica |
E228192
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Garnica |
E228192
|
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: Garnica | Statement: [de Garnica, hasVariant, Garnica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garnica Context triple: [de Garnica, hasVariant, Garnica]
-
A.
de Garnica
chosen
De Garnica is a Spanish surname associated with individuals such as José de Garnica.
-
B.
Gernika-Lumo
Gernika-Lumo is a historic town in the Basque Country of northern Spain, internationally known for the 1937 bombing that inspired Pablo Picasso’s famous painting "Guernica."
-
C.
Albuhera
Albuhera is a village in southwestern Spain that was the site of a major 1811 Peninsular War battle between British-led allied forces and the French.
-
D.
Gironella
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
E.
Manresa
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the 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_69ca846fee388190a6ec273fd644b88b |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fcd9794819093c392489d4efbe9 |
completed | April 1, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12ce37efc81908eee5dd709a574fb |
completed | April 4, 2026, 3:23 p.m. |
Created at: March 30, 2026, 7:53 p.m.