Triple

T267976
Position Surface form Disambiguated ID Type / Status
Subject China Standard Time E5773 entity
Predicate differenceFromUTCInMinutes P5579 FINISHED
Object 480 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: 480 | Statement: [China Standard Time, differenceFromUTCInMinutes, 480]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: differenceFromUTCInMinutes
Context triple: [China Standard Time, differenceFromUTCInMinutes, 480]
  • A. offsetFromUTCInMinutes chosen
    Indicates the number of minutes by which a given time value differs from Coordinated Universal Time (UTC), positive or negative.
  • B. offsetInSecondsFromUTC
    Indicates the time difference, measured in whole seconds, between a given time reference and Coordinated Universal Time (UTC).
  • C. offsetFromUTCInHours
    Indicates the number of hours a time value is offset from Coordinated Universal Time (UTC), positive for time zones ahead and negative for those behind.
  • D. previousUTCOffset
    Indicates the UTC time offset that applied to an entity (such as a timezone or location) immediately before the current or given offset.
  • E. UTCOffsetDaylightSavingTime
    Indicates the time difference from Coordinated Universal Time (UTC) that applies to an entity specifically during daylight saving time periods.
  • F. None of above.

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_69a2587daeb081909591b9d30f80a271 completed Feb. 28, 2026, 2:52 a.m.
NER Named-entity recognition batch_69a25dae4a0c8190a66cf6ed3889851c completed Feb. 28, 2026, 3:14 a.m.
PD Predicate disambiguation batch_69a25b70d99c819085d8381a313a2a34 completed Feb. 28, 2026, 3:05 a.m.
Created at: Feb. 28, 2026, 2:56 a.m.