Triple

T10526566
Position Surface form Disambiguated ID Type / Status
Subject Antonio Fargas E248320 entity
Predicate familyName P18 FINISHED
Object Fargas
Fargas is a surname most notably associated with American actor Antonio Fargas, known for his character roles in film and television.
 E872772 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: Fargas | Statement: [Antonio Fargas, familyName, Fargas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fargas
Context triple: [Antonio Fargas, familyName, Fargas]
  • A. Garmes
    Garmes is a surname most notably associated with Lee Garmes, an influential American cinematographer of Hollywood’s classic era.
  • B. Vauvert
    Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
  • C. Faya-Largeau
    Faya-Largeau is the largest oasis town in northern Chad and an important administrative and trade center in the Sahara Desert.
  • D. Roquebillière
    Roquebillière is a small commune in southeastern France, situated in the Alpes-Maritimes department in the Provence-Alpes-Côte d’Azur region.
  • E. Lavezares
    Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
  • 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: Fargas
Triple: [Antonio Fargas, familyName, Fargas]
Generated description
Fargas is a surname most notably associated with American actor Antonio Fargas, known for his character roles in film and television.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fargas
Target entity description: Fargas is a surname most notably associated with American actor Antonio Fargas, known for his character roles in film and television.
  • A. Garmes
    Garmes is a surname most notably associated with Lee Garmes, an influential American cinematographer of Hollywood’s classic era.
  • B. Vauvert
    Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
  • C. Faya-Largeau
    Faya-Largeau is the largest oasis town in northern Chad and an important administrative and trade center in the Sahara Desert.
  • D. Roquebillière
    Roquebillière is a small commune in southeastern France, situated in the Alpes-Maritimes department in the Provence-Alpes-Côte d’Azur region.
  • E. Lavezares
    Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
  • 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_69d381c5c7448190bec34bee7ec72bac completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d509f4bbe88190bce7789a56c85671 completed April 7, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b1b0de8819089e39ec76e6bdf59 completed April 10, 2026, 7:10 p.m.
NEDg Description generation batch_69d94c6fa9ac8190819a399754d2bd15 completed April 10, 2026, 7:15 p.m.
NED2 Entity disambiguation (via description) batch_69d953440a508190a50d1897cdbeba03 completed April 10, 2026, 7:45 p.m.
Created at: April 6, 2026, 12:29 p.m.