Triple

T31516358
Position Surface form Disambiguated ID Type / Status
Subject Ironheart E804082 entity
Predicate fieldOfExpertiseOfAlterEgo P177172 FINISHED
Object mechanical engineering LITERAL 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: mechanical engineering | Statement: [Ironheart, fieldOfExpertiseOfAlterEgo, mechanical engineering]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: fieldOfExpertiseOfAlterEgo
Context triple: [Ironheart, fieldOfExpertiseOfAlterEgo, mechanical engineering]
  • A. hasFictionalAlterEgoOf
    Indicates that one entity is the fictional alter ego, persona, or alternate identity of another entity.
  • B. knowsSecretIdentityOf
    Indicates that one entity is aware of the hidden or private true identity of another entity.
  • C. roleInComics
    Indicates that an entity holds a specific role or function within the context of comic books or comic-related works.
  • D. isFictionalAgentOf
    Indicates that one entity is a fictional character or agent that acts on behalf of, or represents, another entity.
  • E. hasFictionalSpecialization
    Indicates that an entity’s area of focus, expertise, or role is within a fictional or imaginative domain rather than a real-world specialization.
  • F. None of above. chosen

Provenance (4 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_69f348ceb0a48190ae7feca263b6296c completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6f85bfba48190aba95b40642a8ca7 completed May 3, 2026, 7:25 a.m.
PD Predicate disambiguation batch_69f6f65fd1d08190b88e5e68ba268500 completed May 3, 2026, 7:16 a.m.
PDg Predicate description generation batch_69f6f854486c81909396d944a55e03ab completed May 3, 2026, 7:25 a.m.
Created at: April 30, 2026, 9:53 p.m.