Triple

T12907173
Position Surface form Disambiguated ID Type / Status
Subject Get a Grip E308756 entity
Predicate hasPart P35 FINISHED
Object Flesh E710227 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: Flesh | Statement: [Get a Grip, hasPart, Flesh]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flesh
Context triple: [Get a Grip, hasPart, Flesh]
  • A. Flesh
    Flesh is a science fiction novel by Philip José Farmer that explores themes of sexuality, religion, and societal transformation in a far-future Earth.
  • B. Flesh chosen
    Flesh is a 1968 underground film directed by Paul Morrissey and produced by Andy Warhol, known for its raw, avant-garde portrayal of a male hustler in New York City.
  • C. In the Flesh
    In the Flesh is a British film featuring actress Rita Tushingham in a prominent role.
  • D. Live Flesh
    Live Flesh is a 1997 Spanish drama film by Pedro Almodóvar that intertwines passion, crime, and fate in a story of obsession and redemption set in Madrid.
  • E. Proud Flesh
    Proud Flesh is a photographic series by Sally Mann that features intimate, large-format black-and-white portraits of her husband, exploring themes of aging, vulnerability, and the human body.
  • 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_69d7bdf92b588190acdf2a2291ac4590 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9719d4d1c8190a2c4f362e1772a73 completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6a565ce508190a73f33708e61dc7d completed May 3, 2026, 1:31 a.m.
Created at: April 9, 2026, 5:41 p.m.