Triple
T14412067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abracadabra |
E357353
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Maribel Verdú |
E72383
|
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: Maribel Verdú | Statement: [Abracadabra, starring, Maribel Verdú]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maribel Verdú Context triple: [Abracadabra, starring, Maribel Verdú]
-
A.
Maribel Verdú
chosen
Maribel Verdú is a Spanish actress acclaimed for her work in films such as "Pan’s Labyrinth" and "Y Tu Mamá También."
-
B.
Ana Torrent
Ana Torrent is a Spanish actress best known for her acclaimed childhood performances in films like "The Spirit of the Beehive" and "Cría cuervos."
-
C.
Sara Montiel
Sara Montiel was a celebrated Spanish actress and singer who became an international film star and cultural icon in the mid-20th century.
-
D.
Marta Curro
Marta Curro is an American former actress best known as the first wife of stage and screen actor Jerry Orbach.
-
E.
Paz Vega
Paz Vega is a Spanish actress known for her roles in films such as "Sex and Lucía," "Spanglish," and various international productions.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90cb3c708190822f5506ebf7ee9d |
completed | April 14, 2026, 7:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69febfcd67f081909f97bcf38d814a13 |
completed | May 9, 2026, 5:02 a.m. |
Created at: April 10, 2026, 1:17 a.m.