Triple

T13716820
Position Surface form Disambiguated ID Type / Status
Subject Soviet film industry E328922 entity
Predicate notableFilm P22 FINISHED
Object Stalker E216756 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: Stalker | Statement: [Soviet film industry, notableFilm, Stalker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stalker
Context triple: [Soviet film industry, notableFilm, Stalker]
  • A. Stalker chosen
    Stalker is a 1979 Soviet science fiction art film by Andrei Tarkovsky that follows a guide leading two men through a mysterious forbidden zone said to grant one’s deepest desires.
  • B. Stalker
    Stalker is an American psychological thriller television series centered on a specialized LAPD unit that investigates stalking-related crimes.
  • C. Stalker (TV series)
    Stalker is an American psychological thriller television series that follows a specialized LAPD unit investigating stalking cases, starring Dylan McDermott and Maggie Q.
  • D. Wasteland
    Wasteland is a 1990 American drama film directed by and starring Melanie Mayron, exploring the lives and relationships of a group of young adults.
  • E. Wasteland
    Wasteland is a post-apocalyptic comic series written by Antony Johnston, noted for its bleak world-building and long-form storytelling.
  • 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_69d80770b9bc81909f70c8c317d53cff completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dd4398f0448190810c840a82228706 completed April 13, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69f79d5878948190a2aaab2ba31bd1ed completed May 3, 2026, 7:09 p.m.
Created at: April 9, 2026, 9:54 p.m.