Triple

T14104657
Position Surface form Disambiguated ID Type / Status
Subject Two Trains Running E339473 entity
Predicate hasCharacter P2308 FINISHED
Object Wolf
Wolf is a central character in August Wilson’s play "Two Trains Running," known as a fast-talking numbers runner who operates out of Memphis’s diner in 1960s Pittsburgh.
 E1080913 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: Wolf | Statement: [Two Trains Running, hasCharacter, Wolf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wolf
Context triple: [Two Trains Running, hasCharacter, Wolf]
  • A. Wolf
    Wolf is a common German surname borne by numerous notable individuals across fields such as scholarship, politics, and the arts.
  • B. Wolf
    Wolf is a song by American singer Miguel from his album "War & Leisure."
  • C. Wolf
    Wolf is a 2013 studio album by American rapper and producer Tyler, the Creator, known for its eclectic production and introspective, narrative-driven lyrics.
  • D. Wolf
    The Wolf is a powerful, social canine predator known for living and hunting in packs across the Northern Hemisphere.
  • E. Wolf
    "Wolf" is a 1994 American horror-romance film starring Jack Nicholson and Michelle Pfeiffer, blending werewolf mythology with corporate drama.
  • 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: Wolf
Triple: [Two Trains Running, hasCharacter, Wolf]
Generated description
Wolf is a central character in August Wilson’s play "Two Trains Running," known as a fast-talking numbers runner who operates out of Memphis’s diner in 1960s Pittsburgh.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wolf
Target entity description: Wolf is a central character in August Wilson’s play "Two Trains Running," known as a fast-talking numbers runner who operates out of Memphis’s diner in 1960s Pittsburgh.
  • A. Wolf
    Wolf is a 2013 studio album by American rapper and producer Tyler, the Creator, known for its eclectic production and introspective, narrative-driven lyrics.
  • B. Wolf
    The Wolf is a powerful, social canine predator known for living and hunting in packs across the Northern Hemisphere.
  • C. Wolf
    "Wolf" is a 1994 American horror-romance film starring Jack Nicholson and Michelle Pfeiffer, blending werewolf mythology with corporate drama.
  • D. Wolf
    Wolf is a song by American singer Miguel from his album "War & Leisure."
  • E. Wolf
    Wolf is the silent, one-armed shinobi protagonist of the action-adventure video game Sekiro: Shadows Die Twice.
  • 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_69d81c69b5c8819094aa1abf18302908 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5fbd02888190bf07fd6d8769b61c completed April 14, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd0b48e448190b4fb8cb33e5d97e6 completed May 7, 2026, 5:49 p.m.
NEDg Description generation batch_69fcd288bd5881908f6a442201c5beea completed May 7, 2026, 5:57 p.m.
NED2 Entity disambiguation (via description) batch_69fcd3ad7be8819094fc71c9f44fb4cb completed May 7, 2026, 6:02 p.m.
Created at: April 9, 2026, 10:22 p.m.