Triple

T17171260
Position Surface form Disambiguated ID Type / Status
Subject Lola (Kinky Boots) E416737 entity
Predicate conflictWith P4897 FINISHED
Object Don (factory worker)
Don is a burly, traditionally minded factory worker in the musical "Kinky Boots" who initially clashes with Lola over gender norms and acceptance but ultimately grows to become one of her allies.
 E1254011 NE FINISHED

How this triple was built (4 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: Don (factory worker) | Statement: [Lola (Kinky Boots), conflictWith, Don (factory worker)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Don (factory worker)
Context triple: [Lola (Kinky Boots), conflictWith, Don (factory worker)]
  • A. Donnie
    Donnie is the nickname of Adonis Creed, the central boxer protagonist in the later films of the Rocky/Creed franchise.
  • B. Donnie
    Donnie is a diminutive given name, typically used as a familiar or affectionate form of names like Donna or Donald.
  • C. Dennis
    Dennis is a masculine given name of Greek origin, commonly used in English-speaking countries.
  • D. Dennis
    Dennis is a person or character notably linked to the concept or theme of pests, such as through pest control, infestation, or nuisance-related contexts.
  • E. Dennis
    Dennis is a character associated with Brewis, likely appearing as an ally in the same fictional or narrative context.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Don (factory worker)
Triple: [Lola (Kinky Boots), conflictWith, Don (factory worker)]
Generated description
Don is a burly, traditionally minded factory worker in the musical "Kinky Boots" who initially clashes with Lola over gender norms and acceptance but ultimately grows to become one of her allies.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Don (factory worker)
Target entity description: Don is a burly, traditionally minded factory worker in the musical "Kinky Boots" who initially clashes with Lola over gender norms and acceptance but ultimately grows to become one of her allies.
  • A. Donnie
    Donnie is the nickname of Adonis Creed, the central boxer protagonist in the later films of the Rocky/Creed franchise.
  • B. Donnie
    Donnie is a diminutive given name, typically used as a familiar or affectionate form of names like Donna or Donald.
  • C. Dennis
    Dennis is a masculine given name of Greek origin, commonly used in English-speaking countries.
  • D. Dennis
    Dennis is a person or character notably linked to the concept or theme of pests, such as through pest control, infestation, or nuisance-related contexts.
  • E. Dennis
    Dennis is a character associated with Brewis, likely appearing as an ally in the same fictional or narrative context.
  • F. None of above. chosen

Provenance (5 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_69d886d5f34c8190b24564dfaa63f3fb completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3fc097950819095631ee5679e03af completed April 18, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01483f85648190acaeb197013e1f1b completed May 11, 2026, 3:08 a.m.
NEDg Description generation batch_6a014a1993a48190bf65e590ff57c9c2 completed May 11, 2026, 3:16 a.m.
NED2 Entity disambiguation (via description) batch_6a014a7fa5208190a0a60649fe6292d1 completed May 11, 2026, 3:18 a.m.
Created at: April 10, 2026, 5:37 a.m.