Triple
T6150568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McFarland, USA |
E137188
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Sergio Avelar
Sergio Avelar is an actor known for his role in the inspirational sports drama film "McFarland, USA."
|
E630236
|
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: Sergio Avelar | Statement: [McFarland, USA, starring, Sergio Avelar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sergio Avelar Context triple: [McFarland, USA, starring, Sergio Avelar]
-
A.
Horacio Gutiérrez
Horacio Gutiérrez is a Cuban-American classical pianist renowned for his virtuosic technique and interpretations of the Romantic repertoire.
-
B.
Sergio Palafox
Sergio Palafox is a Mexican sports official known for delivering the officials' Olympic Oath at the 1968 Summer Olympics in Mexico City.
-
C.
Raúl Cárdenas
Raúl Cárdenas was a prominent Mexican football manager best known for leading Club América to multiple league titles during the 1960s and 1970s.
-
D.
Raúl Dávalos
Raúl Dávalos is an editor known for his work on the film "Cronos."
-
E.
Reynaldo Villalobos
Reynaldo Villalobos is a cinematographer best known for his work on notable American films such as the comedy classic "9 to 5."
- 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: Sergio Avelar Triple: [McFarland, USA, starring, Sergio Avelar]
Generated description
Sergio Avelar is an actor known for his role in the inspirational sports drama film "McFarland, USA."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sergio Avelar Target entity description: Sergio Avelar is an actor known for his role in the inspirational sports drama film "McFarland, USA."
-
A.
Horacio Gutiérrez
Horacio Gutiérrez is a Cuban-American classical pianist renowned for his virtuosic technique and interpretations of the Romantic repertoire.
-
B.
Sergio Palafox
Sergio Palafox is a Mexican sports official known for delivering the officials' Olympic Oath at the 1968 Summer Olympics in Mexico City.
-
C.
Raúl Cárdenas
Raúl Cárdenas was a prominent Mexican football manager best known for leading Club América to multiple league titles during the 1960s and 1970s.
-
D.
Raúl Dávalos
Raúl Dávalos is an editor known for his work on the film "Cronos."
-
E.
Reynaldo Villalobos
Reynaldo Villalobos is a cinematographer best known for his work on notable American films such as the comedy classic "9 to 5."
- 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_69c008a45d008190832a9e19f5d63406 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05ce329648190a03ba0233df841fa |
completed | March 22, 2026, 9:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c75800b55081909d6073f10ff16f08 |
completed | March 28, 2026, 4:24 a.m. |
| NEDg | Description generation | batch_69c758cc67d88190a8f9325b757f9baa |
completed | March 28, 2026, 4:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c75943b34481909c4fb40cc22bba3b |
completed | March 28, 2026, 4:29 a.m. |
Created at: March 22, 2026, 4:16 p.m.