Triple

T18147614
Position Surface form Disambiguated ID Type / Status
Subject Thomas Jerome Newton E434427 entity
Predicate romanticRelationshipWith P9994 FINISHED
Object Mary-Lou
Mary-Lou is a human woman who becomes Thomas Jerome Newton’s primary love interest in the story of "The Man Who Fell to Earth."
 E1309704 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: Mary-Lou | Statement: [Thomas Jerome Newton, romanticRelationshipWith, Mary-Lou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mary-Lou
Context triple: [Thomas Jerome Newton, romanticRelationshipWith, Mary-Lou]
  • A. Mary-Lou
    Mary-Lou is a timid, kind-hearted schoolgirl who appears as one of the students in Enid Blyton’s Malory Towers series.
  • B. Mary-Louise
    Mary-Louise is a feminine given name most notably associated with American actress Mary-Louise Parker.
  • C. Mary Lou
    Mary Lou is the first American woman gymnast to win the Olympic all-around gold medal, achieved at the 1984 Los Angeles Games.
  • D. Mary Lou
    Mary Lou is a feminine given name, typically a compound form of Mary and Lou, used in English-speaking countries.
  • E. Mary Lou
    Mary Lou is a technology innovator and entrepreneur best known for her pioneering work in display and imaging technologies, including co-founding One Laptop per Child and founding Openwater.
  • 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: Mary-Lou
Triple: [Thomas Jerome Newton, romanticRelationshipWith, Mary-Lou]
Generated description
Mary-Lou is a human woman who becomes Thomas Jerome Newton’s primary love interest in the story of "The Man Who Fell to Earth."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mary-Lou
Target entity description: Mary-Lou is a human woman who becomes Thomas Jerome Newton’s primary love interest in the story of "The Man Who Fell to Earth."
  • A. Mary-Lou
    Mary-Lou is a timid, kind-hearted schoolgirl who appears as one of the students in Enid Blyton’s Malory Towers series.
  • B. Mary-Louise
    Mary-Louise is a feminine given name most notably associated with American actress Mary-Louise Parker.
  • C. Mary Lou
    Mary Lou is the first American woman gymnast to win the Olympic all-around gold medal, achieved at the 1984 Los Angeles Games.
  • D. Mary Lou
    Mary Lou is a feminine given name, typically a compound form of Mary and Lou, used in English-speaking countries.
  • E. Mary Lou
    Mary Lou is a technology innovator and entrepreneur best known for her pioneering work in display and imaging technologies, including co-founding One Laptop per Child and founding Openwater.
  • 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_69d8b90aac308190801e2c57d8c5bfe5 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4de360ae88190abe1ed13243e9924 completed April 19, 2026, 1:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a038fb549d88190b9e4bf3dbd1c35b5 completed May 12, 2026, 8:38 p.m.
NEDg Description generation batch_6a039182427c81909de840ddf7b2172f completed May 12, 2026, 8:45 p.m.
NED2 Entity disambiguation (via description) batch_6a03927b1794819098106ca3695178dc completed May 12, 2026, 8:50 p.m.
Created at: April 10, 2026, 10:29 a.m.