Triple

T19681433
Position Surface form Disambiguated ID Type / Status
Subject The A Circuit E472598 entity
Predicate mainCharacter P1183 FINISHED
Object Zara
Zara is the determined young equestrian protagonist of Georgina Bloomberg and Catherine Hapka’s novel "The A Circuit," navigating the competitive world of elite show jumping.
 E1389680 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: Zara | Statement: [The A Circuit, mainCharacter, Zara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zara
Context triple: [The A Circuit, mainCharacter, Zara]
  • A. Zara
    Zara is the historical Italian name for the coastal Croatian city of Zadar on the Adriatic Sea.
  • B. Zara
    Zara is a character in the 1953 film noir "Pickup on South Street," involved in the story’s underworld of espionage and crime.
  • C. Zara
    Zara is a global fast-fashion retail brand known for rapidly translating runway trends into affordable clothing and accessories for a mass-market audience.
  • D. Zara
    Zara is a town and district in Turkey known for its location in the eastern part of the Central Anatolia region.
  • E. Zara
    Zara is a British equestrian and member of the royal family, known as the daughter of Princess Anne and granddaughter of Queen Elizabeth II.
  • 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: Zara
Triple: [The A Circuit, mainCharacter, Zara]
Generated description
Zara is the determined young equestrian protagonist of Georgina Bloomberg and Catherine Hapka’s novel "The A Circuit," navigating the competitive world of elite show jumping.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zara
Target entity description: Zara is the determined young equestrian protagonist of Georgina Bloomberg and Catherine Hapka’s novel "The A Circuit," navigating the competitive world of elite show jumping.
  • A. Zara
    Zara is a British equestrian and member of the royal family, known as the daughter of Princess Anne and granddaughter of Queen Elizabeth II.
  • B. Zara
    Zara is a character in the 1953 film noir "Pickup on South Street," involved in the story’s underworld of espionage and crime.
  • C. Zara
    Zara is a town and district in Turkey known for its location in the eastern part of the Central Anatolia region.
  • D. Zara
    Zara is a global fast-fashion retail brand known for rapidly translating runway trends into affordable clothing and accessories for a mass-market audience.
  • E. Zara
    Zara is a station on Milan’s Metro Line 3, serving as a public transportation stop within the city’s underground network.
  • 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_69d8e514f2e08190ba70a4449519d218 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e641bf97348190bc31b00ed4ec6cad completed April 20, 2026, 3:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0787c6bd708190a3693bd3925ac20f completed May 15, 2026, 8:53 p.m.
NEDg Description generation batch_6a07936634a08190a5e90b42f0b34b23 completed May 15, 2026, 9:43 p.m.
NED2 Entity disambiguation (via description) batch_6a0793efdfc08190a2ef81a86ca698c2 completed May 15, 2026, 9:45 p.m.
Created at: April 10, 2026, 1:45 p.m.