Triple

T34795404
Position Surface form Disambiguated ID Type / Status
Subject John Thomas and Lady Jane E1003062 entity
Predicate hasCensorshipHistoryWith P17134 FINISHED
Object Lady Chatterley’s Lover NE NERFINISHED

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: Lady Chatterley’s Lover | Statement: [John Thomas and Lady Jane, hasCensorshipHistoryWith, Lady Chatterley’s Lover]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasCensorshipHistoryWith
Context triple: [John Thomas and Lady Jane, hasCensorshipHistoryWith, Lady Chatterley’s Lover]
  • A. hasCensorshipHistory chosen
    Indicates that an entity has previously been subject to censorship or involved in acts of censoring content.
  • B. hasCensorshipIssue
    Indicates that an entity is subject to, involved in, or associated with censorship or censorship-related concerns.
  • C. hasAccessRestrictionHistory
    Indicates that there exists a record or sequence of past changes detailing how access to something has been restricted over time.
  • D. censorshipStatusAtTime
    Indicates the censorship status of something at a specific point in time, capturing whether and how it was censored then.
  • E. hasCensorshipControversy
    Indicates that an entity has been involved in disputes, criticism, or public debate related to censorship of its content or activities.
  • 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_69f76db543808190b188c6c86a91491b completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69fcd867f36081908c88c55a6a1404c1 completed May 7, 2026, 6:22 p.m.
PD Predicate disambiguation batch_69fcd1f47b188190b4cf4b4c748d9d03 completed May 7, 2026, 5:55 p.m.
Created at: May 3, 2026, 3:59 p.m.