Triple

T16816787
Position Surface form Disambiguated ID Type / Status
Subject Waymond Wang E408771 entity
Predicate hasVariant P455 FINISHED
Object movie-star universe Waymond
Movie-star universe Waymond is an alternate, glamorous version of Waymond Wang who lives a life of fame and sophistication in contrast to his more ordinary counterpart.
E1235491 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: movie-star universe Waymond | Statement: [Waymond Wang, hasVariant, movie-star universe Waymond]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: movie-star universe Waymond
Context triple: [Waymond Wang, hasVariant, movie-star universe Waymond]
  • A. Michael Williams
    Michael Williams was an English actor best known for his work in television, film, and theatre, and for his long marriage to Dame Judi Dench.
  • B. Theron
    Theron is a surname of Greek origin that has been borne by various notable individuals, including actors, athletes, and public figures.
  • C. Theron Warth
    Theron Warth was a film editor known for his work on mid-20th-century American cinema.
  • D. Duron
    Duron is a budget line of x86-compatible microprocessors developed by AMD as a cost-effective alternative to its Athlon series.
  • E. Tudyk
    Tudyk is the surname of Alan Tudyk, an American actor known for his roles in films like "Rogue One" and the series "Firefly."
  • 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: movie-star universe Waymond
Triple: [Waymond Wang, hasVariant, movie-star universe Waymond]
Generated description
Movie-star universe Waymond is an alternate, glamorous version of Waymond Wang who lives a life of fame and sophistication in contrast to his more ordinary counterpart.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: movie-star universe Waymond
Target entity description: Movie-star universe Waymond is an alternate, glamorous version of Waymond Wang who lives a life of fame and sophistication in contrast to his more ordinary counterpart.
  • A. Michael Williams
    Michael Williams was an English actor best known for his work in television, film, and theatre, and for his long marriage to Dame Judi Dench.
  • B. Theron
    Theron is a surname of Greek origin that has been borne by various notable individuals, including actors, athletes, and public figures.
  • C. Theron Warth
    Theron Warth was a film editor known for his work on mid-20th-century American cinema.
  • D. Duron
    Duron is a budget line of x86-compatible microprocessors developed by AMD as a cost-effective alternative to its Athlon series.
  • E. Tudyk
    Tudyk is the surname of Alan Tudyk, an American actor known for his roles in films like "Rogue One" and the series "Firefly."
  • 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_69d88394566c8190b3dcbdc72935f7fa completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b2e1de908190aa3508770fb865cf completed April 18, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b297778c81909a2545c359739151 completed May 10, 2026, 4:30 p.m.
NEDg Description generation batch_6a00b4caae5081909017095977093704 completed May 10, 2026, 4:39 p.m.
NED2 Entity disambiguation (via description) batch_6a00b52f58e08190a28506f03fbeda15 completed May 10, 2026, 4:41 p.m.
Created at: April 10, 2026, 5:23 a.m.