Triple

T11386853
Position Surface form Disambiguated ID Type / Status
Subject Hong Kong Phooey E269733 entity
Predicate mainCharacter P1183 FINISHED
Object Spot the cat
Spot the cat is the loyal yet often more competent feline sidekick to the bumbling superhero Hong Kong Phooey in the classic animated television series.
 E923079 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: Spot the cat | Statement: [Hong Kong Phooey, mainCharacter, Spot the cat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Spot the cat
Context triple: [Hong Kong Phooey, mainCharacter, Spot the cat]
  • A. Catz
    Catz is the informal nickname for St Catharine’s College, one of the constituent colleges of the University of Cambridge.
  • B. Catz
    Catz is the surname of Safra Catz, a prominent business executive best known as the CEO of Oracle Corporation.
  • C. Catz
    Catz is a small commune in the Manche department of northwestern France, situated in the historic Normandy region.
  • D. Cat Cay
    Cat Cay is a small, privately owned island in the Bimini district of the Bahamas, known for its exclusive residential community and yacht harbor.
  • E. Looking for Kitty
    Looking for Kitty is an independent comedy-drama film written and directed by Edward Burns, following a down-on-his-luck private investigator helping a man search for his missing wife in New York City.
  • 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: Spot the cat
Triple: [Hong Kong Phooey, mainCharacter, Spot the cat]
Generated description
Spot the cat is the loyal yet often more competent feline sidekick to the bumbling superhero Hong Kong Phooey in the classic animated television series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Spot the cat
Target entity description: Spot the cat is the loyal yet often more competent feline sidekick to the bumbling superhero Hong Kong Phooey in the classic animated television series.
  • A. Catz
    Catz is the informal nickname for St Catharine’s College, one of the constituent colleges of the University of Cambridge.
  • B. Catz
    Catz is a small commune in the Manche department of northwestern France, situated in the historic Normandy region.
  • C. Catz
    Catz is the surname of Safra Catz, a prominent business executive best known as the CEO of Oracle Corporation.
  • D. Cat Cay
    Cat Cay is a small, privately owned island in the Bimini district of the Bahamas, known for its exclusive residential community and yacht harbor.
  • E. Looking for Kitty
    Looking for Kitty is an independent comedy-drama film written and directed by Edward Burns, following a down-on-his-luck private investigator helping a man search for his missing wife in New York City.
  • 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_69d6aacdbc6c8190af6dc3d5f5d22836 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7fc378d808190b587a044ede67e1e completed April 9, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69e58c3c9a7081908002d726ec9e7715 completed April 20, 2026, 2:15 a.m.
NEDg Description generation batch_69e5932d3cb88190807acdcdc3aaa9fc completed April 20, 2026, 2:45 a.m.
NED2 Entity disambiguation (via description) batch_69e59a0ab7e081908cb8761c4f82c664 completed April 20, 2026, 3:14 a.m.
Created at: April 8, 2026, 9:34 p.m.