Triple

T13860642
Position Surface form Disambiguated ID Type / Status
Subject Guy Gardner E333184 entity
Predicate enemy P4567 FINISHED
Object Sinestro E447849 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: Sinestro | Statement: [Guy Gardner, enemy, Sinestro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sinestro
Context triple: [Guy Gardner, enemy, Sinestro]
  • A. Sinestro chosen
    Sinestro is a powerful former Green Lantern who becomes one of the Corps’ greatest enemies and the archenemy of Hal Jordan in DC Comics.
  • B. Ronan the Accuser
    Ronan the Accuser is a powerful Kree zealot and primary antagonist in the Marvel Cinematic Universe, best known for his role as the villain opposing the Guardians of the Galaxy.
  • C. Mister Negative
    Mister Negative is a Marvel Comics supervillain and crime lord with dual personalities and the power to manipulate dark energy, often serving as a major foe of Spider-Man.
  • D. Absorbing Man
    Absorbing Man is a Marvel Comics supervillain, often depicted as a formidable foe of heroes like Thor and the Hulk, who can magically absorb the properties of anything he touches.
  • E. Mr. Sinister
    Mr. Sinister is a sinister geneticist and long-time X-Men supervillain in Marvel Comics, known for his obsession with mutant experimentation and manipulation.
  • 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_69d81c5ced9c8190b0e9bcc6effe5959 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de02de38e48190b6ead95561031c32 completed April 14, 2026, 9:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0fd3ffc8190965a730843411b80 completed May 3, 2026, 9:41 p.m.
Created at: April 9, 2026, 10:14 p.m.