Triple

T944735
Position Surface form Disambiguated ID Type / Status
Subject Feuillants E20386 entity
Predicate hadPoliticalOrientation P15842 FINISHED
Object anti-radical 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: anti-radical | Statement: [Feuillants, hadPoliticalOrientation, anti-radical]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hadPoliticalOrientation
Context triple: [Feuillants, hadPoliticalOrientation, anti-radical]
  • A. politicalTendency chosen
    Indicates the general political orientation, leaning, or ideological stance associated with an entity in relation to the political spectrum.
  • B. traditionalPoliticalAlignment
    Indicates how closely an entity’s political views or affiliations align with established, historically dominant, or customary political positions within a given context.
  • C. politicalSide
    Indicates the political alignment or ideological position that one entity holds in relation to political spectra or groupings.
  • D. politicalIdentity
    Indicates the political affiliation, ideology, or stance that characterizes an entity’s position within a political spectrum or system.
  • E. hasPoliticalOrientationInJapan
    Indicates that an entity holds or is associated with a specific political orientation within the context of Japan.
  • 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_69a493b0270c81909e6c9ce310f6aa55 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b3a3ed3881908386af140477c514 completed March 1, 2026, 9:46 p.m.
PD Predicate disambiguation batch_69a4b29dc8dc8190b9d33f70f8563d61 completed March 1, 2026, 9:41 p.m.
Created at: March 1, 2026, 7:40 p.m.