Triple

T13174961
Position Surface form Disambiguated ID Type / Status
Subject Justice League: Doom E313074 entity
Predicate editedBy P1954 FINISHED
Object Margaret Hou
Margaret Hou is a film editor known for her work on the animated superhero movie "Justice League: Doom."
 E1196161 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: Margaret Hou | Statement: [Justice League: Doom, editedBy, Margaret Hou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Margaret Hou
Context triple: [Justice League: Doom, editedBy, Margaret Hou]
  • A. Margaret Welsh
    Margaret Welsh is an American actress known for her work in film, television, and theater.
  • B. Margaret Gill
    Margaret Gill was the wife of renowned 19th-century African American Shakespearean actor Ira Aldridge.
  • C. Margaret Chew
    Margaret Chew was the wife of American Revolutionary War officer and Maryland statesman John Eager Howard, connected to the prominent Chew and Howard families of the early United States.
  • D. Margaret Genn
    Margaret Genn was the wife of British actor and barrister Leo Genn.
  • E. Margaret Cox
    Margaret Cox is known as the daughter of British physicist and science communicator Brian Cox.
  • 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: Margaret Hou
Triple: [Justice League: Doom, editedBy, Margaret Hou]
Generated description
Margaret Hou is a film editor known for her work on the animated superhero movie "Justice League: Doom."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Margaret Hou
Target entity description: Margaret Hou is a film editor known for her work on the animated superhero movie "Justice League: Doom."
  • A. Margaret Welsh
    Margaret Welsh is an American actress known for her work in film, television, and theater.
  • B. Margaret Gill
    Margaret Gill was the wife of renowned 19th-century African American Shakespearean actor Ira Aldridge.
  • C. Margaret Chew
    Margaret Chew was the wife of American Revolutionary War officer and Maryland statesman John Eager Howard, connected to the prominent Chew and Howard families of the early United States.
  • D. Margaret Genn
    Margaret Genn was the wife of British actor and barrister Leo Genn.
  • E. Margaret Cox
    Margaret Cox is known as the daughter of British physicist and science communicator Brian Cox.
  • 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_69d806ac3ee081909b2fd27d060aa974 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c303e3c819086cf0f0b6d9e61ca completed April 10, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fff288ba908190a36c4784331d1e60 completed May 10, 2026, 2:50 a.m.
NEDg Description generation batch_69fff3806ab08190b2450b0f1f4bfc3c completed May 10, 2026, 2:54 a.m.
NED2 Entity disambiguation (via description) batch_69fff3f2760c8190a58fedc2798614ae completed May 10, 2026, 2:56 a.m.
Created at: April 9, 2026, 9:14 p.m.