Triple

T3113782
Position Surface form Disambiguated ID Type / Status
Subject Jesse Eisenberg E65009 entity
Predicate notableWork P4 FINISHED
Object Zombieland E142421 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: Zombieland | Statement: [Jesse Eisenberg, notableWork, Zombieland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zombieland
Context triple: [Jesse Eisenberg, notableWork, Zombieland]
  • A. Zombieland chosen
    Zombieland is a 2009 horror-comedy film about a group of survivors navigating a zombie apocalypse with a mix of humor, gore, and quirky survival rules.
  • B. Zombies
    Zombies is a popular cooperative survival-horror game mode featuring waves of undead enemies, best known from its inclusion in Treyarch’s Call of Duty titles.
  • C. Zombie
    A Zombie in Minecraft is a common hostile undead mob that attacks players and villagers, often spawning in dark areas and burning in sunlight.
  • D. World War Z
    World War Z is a 2013 apocalyptic action-horror film starring Brad Pitt that depicts a global zombie pandemic and humanity’s desperate efforts to survive.
  • E. The Cabin in the Woods
    The Cabin in the Woods is a 2012 meta-horror film that deconstructs and satirizes classic horror movie tropes through a self-aware, genre-bending storyline.
  • 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_69ad857fcc088190b0c4d45a5cde6f61 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada43c79448190aa72f707319e8c5e completed March 8, 2026, 4:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2039b11d4819095ee77d84d6e7b8a completed March 12, 2026, 12:06 a.m.
Created at: March 8, 2026, 3:04 p.m.