Triple

T1579844
Position Surface form Disambiguated ID Type / Status
Subject Lord Lieutenant of Lancashire E33735 entity
Predicate remuneration P14176 FINISHED
Object unpaid office 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: unpaid office | Statement: [Lord Lieutenant of Lancashire, remuneration, unpaid office]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: remuneration
Context triple: [Lord Lieutenant of Lancashire, remuneration, unpaid office]
  • A. salary
    Indicates the amount of monetary compensation an entity receives, typically on a regular basis, for work or services performed.
  • B. compensationCategory chosen
    Indicates the type or classification of compensation associated with an entity, such as how or in what form payment or remuneration is provided.
  • C. compensated
    Indicates that one entity provides payment or some form of recompense to another entity in return for goods, services, or loss incurred.
  • D. incomeTreatment
    Indicates a relationship where an entity receives, is subject to, or is affected by a particular income-related treatment, policy, or classification.
  • E. revenue
    Indicates the amount of income generated by an entity from its business activities or operations over a specified period.
  • 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_69a885f27a4c8190a4622252cdf54c00 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abacfb1144819080c5687175aba1e1 completed March 7, 2026, 4:43 a.m.
PD Predicate disambiguation batch_69aa61b0f5bc8190b1dc272990a59c13 completed March 6, 2026, 5:10 a.m.
Created at: March 4, 2026, 7:27 p.m.