Triple

T16622298
Position Surface form Disambiguated ID Type / Status
Subject Diane Salinger E403863 entity
Predicate notableWork P4 FINISHED
Object Creature E792990 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: Creature | Statement: [Diane Salinger, notableWork, Creature]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Creature
Context triple: [Diane Salinger, notableWork, Creature]
  • A. Creature chosen
    "Creature" is a horror film featuring actor Cory Hardrict in a prominent role.
  • B. Wesen
    Wesen are supernatural creatures from the TV series "Grimm," each with a hidden animalistic form and unique abilities that coexist secretly alongside humans.
  • C. Mi-go
    Mi-go are extraterrestrial, fungus-like beings from H. P. Lovecraft’s Cthulhu Mythos, known for their grotesque appearance, advanced alien technology, and sinister experiments on human brains.
  • D. Creatures
    Creatures is a Japanese video game and entertainment company best known for its major role in developing and managing the Pokémon franchise, including games, trading cards, and related media.
  • E. MUTO
    MUTO is a giant parasitic kaiju species in the 2014 film "Godzilla," known for feeding on nuclear material and serving as one of Godzilla’s primary adversaries.
  • 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_69d883897eb481909eaaa088ba9918d9 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3754e80ec8190b3c66b33dbc7463c completed April 18, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a007db27f788190a3c57b7ea8a8a9c6 completed May 10, 2026, 12:44 p.m.
Created at: April 10, 2026, 5:17 a.m.