Triple

T19111925
Position Surface form Disambiguated ID Type / Status
Subject linear temporal logic E467810 entity
Predicate hasAbbreviation P43 FINISHED
Object LTL NE NERFINISHED

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: LTL | Statement: [linear temporal logic, hasAbbreviation, LTL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LTL
Context triple: [linear temporal logic, hasAbbreviation, LTL]
  • A. LTL
    LTL is the former official currency code for the Lithuanian litas, which was replaced by the euro in 2015.
  • B. LTL chosen
    LTL (Linear Temporal Logic) is a formalism used in computer science and logic to specify and reason about the temporal ordering of events along linear time, particularly in the verification of reactive and concurrent systems.
  • C. LTL
    LTL is the National Rail station code for Littleborough railway station in Greater Manchester, England.
  • D. TLT
    TLT is the time zone abbreviation used for Timor Leste Time, the standard time observed in East Timor.
  • E. TLT
    TLT is the IATA airport code for the small public airport serving the remote community of Tuluksak in western Alaska.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd06a26481908039e2a1bae8c597 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e394969c81909d09b2300ea0e041 completed April 20, 2026, 8:28 a.m.
Created at: April 10, 2026, 12:04 p.m.