Triple

T14537112
Position Surface form Disambiguated ID Type / Status
Subject Tavi Gevinson E341074 entity
Predicate employer P7 FINISHED
Object Rookie E1104463 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: Rookie | Statement: [Tavi Gevinson, employer, Rookie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rookie
Context triple: [Tavi Gevinson, employer, Rookie]
  • A. Rookie chosen
    Rookie is an online magazine and community for teenage girls founded by writer and actress Tavi Gevinson, known for its focus on feminism, pop culture, and youth culture.
  • B. the Rookie
    The Rookie is the silent Orbital Drop Shock Trooper player character in Halo 3: ODST, known for navigating a ruined New Mombasa to piece together his squad’s fate.
  • C. Entered Apprentice
    Entered Apprentice is the first and introductory degree of Freemasonry, representing a candidate’s initial initiation into the Masonic fraternity.
  • D. Junior
    Junior is a 1994 comedy film in which Arnold Schwarzenegger plays a scientist who becomes pregnant as part of an experimental fertility project.
  • E. Junior
    Junior is the protagonist of the novel "Love" by Toni Morrison, around whom the story’s complex relationships and themes of desire, memory, and power revolve.
  • 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_69d822dac79c8190a84a073f3cbaced5 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb1bb90008190947ac0961393446d completed April 14, 2026, 9:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd94acd8288190a91bf09220126e13 completed May 8, 2026, 7:45 a.m.
Created at: April 10, 2026, 1:22 a.m.