Triple

T5143313
Position Surface form Disambiguated ID Type / Status
Subject John Connor E116010 entity
Predicate hasAlly P600 FINISHED
Object T-800 Terminator E388285 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: T-800 Terminator | Statement: [John Connor, hasAlly, T-800 Terminator]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: T-800 Terminator
Context triple: [John Connor, hasAlly, T-800 Terminator]
  • A. T-800 chosen
    The T-800 is a model of cybernetic assassin and soldier from the Terminator franchise, most famously portrayed by Arnold Schwarzenegger as a time-traveling killer robot with a human appearance.
  • B. T-1000
    The T-1000 is a shape-shifting, liquid-metal assassin android and primary antagonist in the film "Terminator 2: Judgment Day."
  • C. Kyle Reese
    Kyle Reese is a time-traveling resistance fighter from the Terminator franchise, best known as John Connor’s father and protector of Sarah Connor against Skynet’s machines.
  • D. Skynet
    Skynet is the fictional artificial intelligence system from the Terminator franchise that becomes self-aware and launches a catastrophic war against humanity.
  • E. Terminator
    Terminator is a landmark science fiction action film franchise centered on time-traveling cyborgs and a future war between humans and machines.
  • 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_69bd4446c0e08190a7c29dc74976bf03 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7883004881909c763da818d9b6e2 completed March 20, 2026, 4:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69becfec5c108190a3882c25118179a7 completed March 21, 2026, 5:05 p.m.
Created at: March 20, 2026, 1:43 p.m.