Triple

T5582303
Position Surface form Disambiguated ID Type / Status
Subject Maclisp E146667 entity
Predicate platform P1292 FINISHED
Object TENEX E431123 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: TENEX | Statement: [Maclisp, platform, TENEX]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TENEX
Context triple: [Maclisp, platform, TENEX]
  • A. TENEX operating system chosen
    TENEX operating system is an early time-sharing operating system for the PDP-10 that introduced advanced virtual memory and interactive computing features influential in later systems.
  • B. TX-10
    TX-10 is the commonly used abbreviation for Texas's 10th congressional district, a U.S. House of Representatives district covering parts of central Texas.
  • C. Teldec
    Teldec was a prominent German classical music record label known for its high-quality recordings and influential catalog of orchestral and early music.
  • D. Tandy
    Tandy is a surname most notably associated with Jessica Tandy, the acclaimed British-American actress known for her work on stage and in film.
  • E. TREX
    TREX is an XML schema language designed by James Clark that served as a precursor to the RELAX NG schema specification.
  • 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_69c0090287a08190b4098411effe970c completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0208333f08190bf0049b6bdd280f5 completed March 22, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0285e7bc08190bd5a08c50679e9d9 completed March 22, 2026, 5:35 p.m.
Created at: March 22, 2026, 3:37 p.m.