Triple

T2778675
Position Surface form Disambiguated ID Type / Status
Subject Regius Professor of Modern History at the University of Cambridge E61637 entity
Predicate hasTemporalFocus P106 FINISHED
Object modern period 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: modern period | Statement: [Regius Professor of Modern History at the University of Cambridge, hasTemporalFocus, modern period]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasTemporalFocus
Context triple: [Regius Professor of Modern History at the University of Cambridge, hasTemporalFocus, modern period]
  • A. temporalAspect
    Indicates the time-related characteristics or phase (such as duration, frequency, or temporal status) associated with an event or relationship.
  • B. hasTemporalLocation
    Indicates that something occurs, exists, or is valid during a specific time or time interval.
  • C. focusPeriod chosen
    Indicates the specific time span during which attention, activity, or analysis is concentrated on something.
  • D. temporalRelation
    Indicates a relationship that specifies how two events or states are positioned relative to each other in time (e.g., before, after, or overlapping).
  • E. timePerspective
    Indicates how an entity conceptually relates to or orients itself toward time, such as focusing on past, present, or future.
  • 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_69ab4b7e43c48190997b8fc8fb1663ab completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abddceb9d88190961e30d521a21552 completed March 7, 2026, 8:11 a.m.
PD Predicate disambiguation batch_69abdd00b65c8190a8ea444308c4fa2b completed March 7, 2026, 8:08 a.m.
Created at: March 6, 2026, 9:57 p.m.