Triple

T3545325
Position Surface form Disambiguated ID Type / Status
Subject John Ritter E74981 entity
Predicate appearedIn P795 FINISHED
Object Hooperman
Hooperman is an American television dramedy series from the late 1980s starring John Ritter as a San Francisco police inspector balancing his personal and professional life.
 E368311 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: Hooperman | Statement: [John Ritter, appearedIn, Hooperman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hooperman
Context triple: [John Ritter, appearedIn, Hooperman]
  • A. Carver
    Carver is a surname most notably associated with American short story writer and poet Raymond Carver, a key figure in late 20th-century minimalist fiction.
  • B. Carver
    Carver is the person after whom Carver Glacier in Oregon was named, likely an early explorer or notable figure associated with the region.
  • C. Carver
    Carver is a small town in southeastern Massachusetts known for its cranberry bogs and rural character.
  • D. Carver
    Carver is a common shorthand name for Carver-Hawkeye Arena, the University of Iowa’s primary indoor sports venue.
  • E. Herman
    Herman is a surname most notably associated with Edward S. Herman, an American economist, media analyst, and critic of U.S. foreign policy.
  • 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: Hooperman
Triple: [John Ritter, appearedIn, Hooperman]
Generated description
Hooperman is an American television dramedy series from the late 1980s starring John Ritter as a San Francisco police inspector balancing his personal and professional life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hooperman
Target entity description: Hooperman is an American television dramedy series from the late 1980s starring John Ritter as a San Francisco police inspector balancing his personal and professional life.
  • A. Carver
    Carver is a surname most notably associated with American short story writer and poet Raymond Carver, a key figure in late 20th-century minimalist fiction.
  • B. Carver
    Carver is the person after whom Carver Glacier in Oregon was named, likely an early explorer or notable figure associated with the region.
  • C. Carver
    Carver is a small town in southeastern Massachusetts known for its cranberry bogs and rural character.
  • D. Carver
    Carver is a common shorthand name for Carver-Hawkeye Arena, the University of Iowa’s primary indoor sports venue.
  • E. Herman
    Herman is a surname most notably associated with Edward S. Herman, an American economist, media analyst, and critic of U.S. foreign policy.
  • 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_69ad85d274cc8190ab59c97298a1cfbf completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbf77d938819095c72a88b5af644a completed March 8, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b38bdfe5748190b2de831635221ce1 completed March 13, 2026, 4 a.m.
NEDg Description generation batch_69b38fda21fc81909b9ded36239abf93 completed March 13, 2026, 4:17 a.m.
NED2 Entity disambiguation (via description) batch_69b39049e9cc81909ad74d33afe1985e completed March 13, 2026, 4:19 a.m.
Created at: March 8, 2026, 3:20 p.m.