Triple

T15165422
Position Surface form Disambiguated ID Type / Status
Subject Laughing Matter E362326 entity
Predicate hasPart P35 FINISHED
Object Hare
Hare is a fast-running, long-eared mammal resembling a large rabbit, known for its powerful hind legs and solitary, open-country lifestyle.
 E1142469 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: Hare | Statement: [Laughing Matter, hasPart, Hare]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hare
Context triple: [Laughing Matter, hasPart, Hare]
  • A. Hare
    Hare is a common English surname borne by various notable individuals across fields such as politics, religion, and the arts.
  • B. Rabbit
    Rabbit Maranville was a Hall of Fame Major League Baseball shortstop known for his exceptional fielding, durability, and colorful personality in the early 20th century.
  • C. Rabbit
    Rabbit is a famous stainless-steel sculpture by Jeff Koons, celebrated as an iconic work of contemporary pop and conceptual art.
  • D. Rabbit
    Rabbit is a fussy, practical, and often bossy animal character from A. A. Milne’s Winnie-the-Pooh stories, known for trying to keep order in the Hundred Acre Wood.
  • E. Rabbit
    Rabbit is a high-speed stream cipher designed for efficient software implementation, particularly suited for environments with limited resources.
  • 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: Hare
Triple: [Laughing Matter, hasPart, Hare]
Generated description
Hare is a fast-running, long-eared mammal resembling a large rabbit, known for its powerful hind legs and solitary, open-country lifestyle.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hare
Target entity description: Hare is a fast-running, long-eared mammal resembling a large rabbit, known for its powerful hind legs and solitary, open-country lifestyle.
  • A. Hare
    Hare is a common English surname borne by various notable individuals across fields such as politics, religion, and the arts.
  • B. Rabbit
    Rabbit Maranville was a Hall of Fame Major League Baseball shortstop known for his exceptional fielding, durability, and colorful personality in the early 20th century.
  • C. Rabbit
    Rabbit is a fussy, practical, and often bossy animal character from A. A. Milne’s Winnie-the-Pooh stories, known for trying to keep order in the Hundred Acre Wood.
  • D. Rabbit
    Rabbit is a famous stainless-steel sculpture by Jeff Koons, celebrated as an iconic work of contemporary pop and conceptual art.
  • E. Rabbit
    Rabbit is a high-speed stream cipher designed for efficient software implementation, particularly suited for environments with limited resources.
  • 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_69d85a087b7c81908baa94a53dac8d68 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0064c6244819085daf8e1eafdf3f2 completed April 15, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69fec885d68c8190999529b69bc34fec completed May 9, 2026, 5:39 a.m.
NEDg Description generation batch_69fec93109c08190a3499e4520e31604 completed May 9, 2026, 5:42 a.m.
NED2 Entity disambiguation (via description) batch_69fecc6fa8f88190aa6956e6e2b1f8ab completed May 9, 2026, 5:55 a.m.
Created at: April 10, 2026, 3:08 a.m.