Triple

T33700456
Position Surface form Disambiguated ID Type / Status
Subject The Recruiting Officer E863440 entity
Predicate hasFemaleCharacterInMaleDisguise P146968 FINISHED
Object Sylvia
Sylvia is a witty, resourceful heroine from the Restoration comedy "The Recruiting Officer" who disguises herself as a man to pursue love and autonomy in a male-dominated society.
 E863443 NE FINISHED

How this triple was built (3 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: Sylvia | Statement: [The Recruiting Officer, hasFemaleCharacterInMaleDisguise, Sylvia]
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: Sylvia
Triple: [The Recruiting Officer, hasFemaleCharacterInMaleDisguise, Sylvia]
Generated description
Sylvia is a witty, resourceful heroine from the Restoration comedy "The Recruiting Officer" who disguises herself as a man to pursue love and autonomy in a male-dominated society.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasFemaleCharacterInMaleDisguise
Context triple: [The Recruiting Officer, hasFemaleCharacterInMaleDisguise, Sylvia]
  • A. hasCrossDressingProtagonist chosen
    Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
  • B. hasFemaleCharacter
    Indicates that an entity includes or features at least one female character.
  • C. hasFemaleEquivalent
    Indicates that one entity serves as the female counterpart or equivalent of another entity.
  • D. hasLeadCharacterGender
    Indicates that the primary or lead character in a work has a specified gender.
  • E. isMaleCharacter
    Indicates that the referenced character is identified as male.
  • F. None of above.

Provenance (6 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_69f3498723a08190ac034339cc78eade completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f7b5ccbda481908fe1945c35e36ce8 completed May 3, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a365c72f0048190807c2ae8294c4a80 completed June 20, 2026, 9:25 a.m.
NEDg Description generation batch_6a365cd7639081909ac1baf5299bc695 completed June 20, 2026, 9:26 a.m.
NED2 Entity disambiguation (via description) batch_6a365e2cb78c819081d7a30b7a129630 completed June 20, 2026, 9:32 a.m.
PD Predicate disambiguation batch_69f7b4c06f5881908f0b98cad6796478 completed May 3, 2026, 8:49 p.m.
Created at: May 1, 2026, 1:43 a.m.