Triple

T13924235
Position Surface form Disambiguated ID Type / Status
Subject BPS-10 E334820 entity
Predicate typicalPositionType P112307 FINISHED
Object administrative positions 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: administrative positions | Statement: [BPS-10, typicalPositionType, administrative positions]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: typicalPositionType
Context triple: [BPS-10, typicalPositionType, administrative positions]
  • A. typicalRecipientPosition
    Indicates the usual or expected spatial or organizational position where a recipient is located relative to the action or source.
  • B. typicalPositionBias
    Indicates a systematic tendency for something to be placed, chosen, or interpreted in a commonly favored or default position relative to other options.
  • C. typicalCalendarPosition
    Indicates the usual or standard placement of something within a calendar sequence, such as its customary date, time, or ordering.
  • D. typicalBalancingPositions
    Indicates the standard or commonly used positions in which elements are arranged or adjusted to achieve balance or equilibrium in a given context.
  • E. typicalPositionInSentence
    Indicates the usual or most common position that an element occupies within the linear order of components in a sentence.
  • F. None of above. chosen

Provenance (4 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_69d81c5f739081908bc05b2461f54828 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2aa6cd9881908f652538f4613f37 completed April 14, 2026, 11:53 a.m.
PD Predicate disambiguation batch_69de059e4ba881908554f72e889719fa completed April 14, 2026, 9:15 a.m.
PDg Predicate description generation batch_69de239524688190a0f2408c239cfcaa completed April 14, 2026, 11:23 a.m.
Created at: April 9, 2026, 10:16 p.m.