Triple
T14655725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julieta |
E344101
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Daniel Grao
Daniel Grao is a Spanish film, television, and stage actor known for his roles in works by director Pedro Almodóvar and various acclaimed Spanish TV series.
|
E1112935
|
NE FINISHED |
How this triple was built (4 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: Daniel Grao | Statement: [Julieta, castMember, Daniel Grao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Grao Context triple: [Julieta, castMember, Daniel Grao]
-
A.
John Duarte
John Duarte is a Republican politician and businessman serving as the U.S. Representative for California's 13th congressional district.
-
B.
Daniel Morales
Daniel Morales is the reckless yet highly skilled Marseille taxi driver and protagonist of the French action-comedy Taxi film series.
-
C.
Gonzalo Vivanco
Gonzalo Vivanco is a Chilean actor known for his roles in Latin American television series and telenovelas.
-
D.
Mark Vicente
Mark Vicente is a cinematographer and filmmaker best known for his work on the documentary "What the Bleep Do We Know!?" and his involvement in the NXIVM organization.
-
E.
Carlos Vega
Carlos Vega was a highly respected American session drummer known for his versatile work across pop, rock, and jazz recordings.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Daniel Grao Triple: [Julieta, castMember, Daniel Grao]
Generated description
Daniel Grao is a Spanish film, television, and stage actor known for his roles in works by director Pedro Almodóvar and various acclaimed Spanish TV series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Grao Target entity description: Daniel Grao is a Spanish film, television, and stage actor known for his roles in works by director Pedro Almodóvar and various acclaimed Spanish TV series.
-
A.
John Duarte
John Duarte is a Republican politician and businessman serving as the U.S. Representative for California's 13th congressional district.
-
B.
Daniel Morales
Daniel Morales is the reckless yet highly skilled Marseille taxi driver and protagonist of the French action-comedy Taxi film series.
-
C.
Gonzalo Vivanco
Gonzalo Vivanco is a Chilean actor known for his roles in Latin American television series and telenovelas.
-
D.
Mark Vicente
Mark Vicente is a cinematographer and filmmaker best known for his work on the documentary "What the Bleep Do We Know!?" and his involvement in the NXIVM organization.
-
E.
Carlos Vega
Carlos Vega was a highly respected American session drummer known for his versatile work across pop, rock, and jazz recordings.
- F. None of above. chosen
Provenance (5 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51a562c819098971447db4b29f7 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5de0b98819094c32765e4cb3f9c |
completed | May 8, 2026, 12:23 p.m. |
| NEDg | Description generation | batch_69fddd8d7da481909d38d9390770939c |
completed | May 8, 2026, 12:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdde23da708190b7eabeed6a9cb169 |
completed | May 8, 2026, 12:59 p.m. |
Created at: April 10, 2026, 1:27 a.m.