Triple

T15311976
Position Surface form Disambiguated ID Type / Status
Subject Mud E366058 entity
Predicate characterRole P268 FINISHED
Object Neckbone
Neckbone is a character in the film "Mud," serving as one of the two young boys who befriend the title character and help drive the story’s coming-of-age narrative.
 E1150736 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: Neckbone | Statement: [Mud, characterRole, Neckbone]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Neckbone
Context triple: [Mud, characterRole, Neckbone]
  • A. Shinbone
    Shinbone is the fictional frontier town in the classic Western film "The Man Who Shot Liberty Valance," serving as the central setting for its story of law, legend, and political change.
  • B. Hals
    Hals is a renowned Dutch Golden Age painter, best known for his lively and expressive portraiture.
  • C. Hals
    Hals is a small Danish coastal town situated at the eastern entrance of the Limfjord, known for its maritime setting and local harbor.
  • D. Bone
    Bone is a hard, calcified connective tissue that forms the structural framework of the skeleton in vertebrate animals.
  • E. Bone
    The B-1B Lancer, nicknamed "Bone," is a U.S. long-range supersonic strategic bomber known for its variable-sweep wings and low-level penetration capabilities.
  • 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: Neckbone
Triple: [Mud, characterRole, Neckbone]
Generated description
Neckbone is a character in the film "Mud," serving as one of the two young boys who befriend the title character and help drive the story’s coming-of-age narrative.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Neckbone
Target entity description: Neckbone is a character in the film "Mud," serving as one of the two young boys who befriend the title character and help drive the story’s coming-of-age narrative.
  • A. Shinbone
    Shinbone is the fictional frontier town in the classic Western film "The Man Who Shot Liberty Valance," serving as the central setting for its story of law, legend, and political change.
  • B. Hals
    Hals is a renowned Dutch Golden Age painter, best known for his lively and expressive portraiture.
  • C. Hals
    Hals is a small Danish coastal town situated at the eastern entrance of the Limfjord, known for its maritime setting and local harbor.
  • D. Bone
    Bone is a hard, calcified connective tissue that forms the structural framework of the skeleton in vertebrate animals.
  • E. Bone
    The B-1B Lancer, nicknamed "Bone," is a U.S. long-range supersonic strategic bomber known for its variable-sweep wings and low-level penetration capabilities.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03cd2d5a88190aead748920f93d47 completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69fef8a3da3881909b50cfbec0543adc completed May 9, 2026, 9:04 a.m.
NEDg Description generation batch_69fefdb82b2081908084a12a58ad3477 completed May 9, 2026, 9:26 a.m.
NED2 Entity disambiguation (via description) batch_69fefe6c42708190bd893885fc5bc88e completed May 9, 2026, 9:29 a.m.
Created at: April 10, 2026, 3:16 a.m.