Triple

T2467634
Position Surface form Disambiguated ID Type / Status
Subject Wuhan–Guangzhou High-Speed Railway E55289 entity
Predicate previousTravelTime P22194 FINISHED
Object over 10 hours between Wuhan and Guangzhou 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: over 10 hours between Wuhan and Guangzhou | Statement: [Wuhan–Guangzhou High-Speed Railway, previousTravelTime, over 10 hours between Wuhan and Guangzhou]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: previousTravelTime
Context triple: [Wuhan–Guangzhou High-Speed Railway, previousTravelTime, over 10 hours between Wuhan and Guangzhou]
  • A. previousLocationTime
    Indicates the specific time at which an entity was located at its immediately preceding location before its current one.
  • B. travelTimeCategory chosen
    Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
  • C. timeTravelFrom
    Indicates a relationship where an entity initiates time travel starting from a specific time or temporal location.
  • D. distanceTraveled
    Indicates the total length of the path an entity has moved over a period of time or between two points.
  • E. passesUsedForTransportation
    Indicates that the passes are utilized as a means or instrument for transporting people or goods.
  • 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_69ab49e3622c8190ad22afa2c4fbb807 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd2bc7b5481908b3664495e99f1a4 completed March 7, 2026, 7:24 a.m.
PD Predicate disambiguation batch_69abd0b3ea308190a6d8499c2a542c50 completed March 7, 2026, 7:16 a.m.
Created at: March 6, 2026, 9:44 p.m.