Triple
T21027506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alba Trueba |
E517977
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Trueba |
—
|
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: Trueba | Statement: [Alba Trueba, familyName, Trueba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trueba Context triple: [Alba Trueba, familyName, Trueba]
-
A.
Trueba
chosen
Trueba is a Spanish surname most notably associated with acclaimed film director and screenwriter Fernando Trueba.
-
B.
Nabeal
Nabeal is a given name and variant spelling of Nabeil, typically used as a personal name in Arabic-speaking or Muslim communities.
-
C.
Tabárez
Tabárez is the surname of Óscar Tabárez, the renowned Uruguayan football manager best known for his long and successful tenure coaching the Uruguay national team.
-
D.
Balbuena
Balbuena is a metro station on Mexico City’s Line 1 serving the Balbuena neighborhood in the eastern part of the city.
-
E.
Talino
Talino is the protagonist of the Italian work "Paesi tuoi," around whom the story’s central events and themes revolve.
- 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_69e0b503275c8190afd9a163f997c709 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc7d93908190a2c29a4051fb5acc |
completed | April 21, 2026, 4:26 a.m. |
Created at: April 16, 2026, 1:55 p.m.