Triple

T32250794
Position Surface form Disambiguated ID Type / Status
Subject Convicted E823873 entity
Predicate hasBroderickCrawfordRole P181176 FINISHED
Object George Knowland
George Knowland is a character portrayed by Broderick Crawford in the 1950 film noir crime drama "Convicted."
 E2001363 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: George Knowland | Statement: [Convicted, hasBroderickCrawfordRole, George Knowland]
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: George Knowland
Triple: [Convicted, hasBroderickCrawfordRole, George Knowland]
Generated description
George Knowland is a character portrayed by Broderick Crawford in the 1950 film noir crime drama "Convicted."
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasBroderickCrawfordRole
Context triple: [Convicted, hasBroderickCrawfordRole, George Knowland]
  • A. hasJoanFontaineRole
    Indicates that an entity has a role played by Joan Fontaine in a film, television, or theatrical production.
  • B. hasElizabethTaylorRole
    Indicates that an entity has a role that was originally played by, associated with, or famously portrayed by Elizabeth Taylor.
  • C. hasGingerRogersRole
    Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
  • D. hasRitaHayworthRole
    Indicates that an entity has a role or character associated with Rita Hayworth, such as portraying her or a role closely linked to her.
  • E. hasGlennFordRole
    Indicates that an entity has a role played by the actor Glenn Ford.
  • F. None of above. chosen

Provenance (7 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_69f3490cdda88190a9d61e11252a771f completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f7626667f48190ad90867eb67ec582 completed May 3, 2026, 2:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3056f7f2748190bd1715dfd5eced32 completed June 15, 2026, 7:48 p.m.
NEDg Description generation batch_6a305835ae0c8190b88773e510826a0d completed June 15, 2026, 7:53 p.m.
NED2 Entity disambiguation (via description) batch_6a3058b2245c8190811b6cdad0f18aa6 completed June 15, 2026, 7:55 p.m.
PD Predicate disambiguation batch_69f76175d6608190b60b268e20f49ed9 completed May 3, 2026, 2:53 p.m.
PDg Predicate description generation batch_69f762651e088190baa21f25378a6065 completed May 3, 2026, 2:57 p.m.
Created at: May 1, 2026, 12:40 a.m.