Triple

T22947896
Position Surface form Disambiguated ID Type / Status
Subject Herman's Head E569922 entity
Predicate character P662 FINISHED
Object Mr. Amber
Mr. Amber is a fictional character from the early-1990s American sitcom "Herman's Head," which explored the inner thoughts and emotions of its main character.
 E1564295 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: Mr. Amber | Statement: [Herman's Head, character, Mr. Amber]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mr. Amber
Context triple: [Herman's Head, character, Mr. Amber]
  • A. Mr. Bristal
    Mr. Bristal is a musical artist known for contributing to the project "The Wonderful World of Cease A Leo."
  • B. Mr. Bryles
    Mr. Bryles is the central character in the 1999-set story "Class of 1999," around whom the main events and conflicts revolve.
  • C. Mr. Gar
    Mr. Gar is a tough, muscular former hero who runs the Lakewood Plaza Turbo bodega and mentors the young protagonist in the animated series "OK K.O.! Let's Be Heroes."
  • D. Mr. Rout
    Mr. Rout is the central figure in Joseph Conrad’s short story "Typhoon," serving as the steadfast sea captain whose leadership is tested by a violent storm at sea.
  • E. Mr. Brown
    Mr. Brown is the kind-hearted but often flustered father figure from the "Paddington" film series.
  • 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: Mr. Amber
Triple: [Herman's Head, character, Mr. Amber]
Generated description
Mr. Amber is a fictional character from the early-1990s American sitcom "Herman's Head," which explored the inner thoughts and emotions of its main character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mr. Amber
Target entity description: Mr. Amber is a fictional character from the early-1990s American sitcom "Herman's Head," which explored the inner thoughts and emotions of its main character.
  • A. Mr. Bristal
    Mr. Bristal is a musical artist known for contributing to the project "The Wonderful World of Cease A Leo."
  • B. Mr. Bryles
    Mr. Bryles is the central character in the 1999-set story "Class of 1999," around whom the main events and conflicts revolve.
  • C. Mr. Gar
    Mr. Gar is a tough, muscular former hero who runs the Lakewood Plaza Turbo bodega and mentors the young protagonist in the animated series "OK K.O.! Let's Be Heroes."
  • D. Mr. Rout
    Mr. Rout is the central figure in Joseph Conrad’s short story "Typhoon," serving as the steadfast sea captain whose leadership is tested by a violent storm at sea.
  • E. Mr. Brown
    Mr. Brown is the kind-hearted but often flustered father figure from the "Paddington" film series.
  • 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_69e2459199d08190a8184ee2aa935842 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1819fbf8c8190ad80c93f1507aa73 completed April 29, 2026, 3:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bca1437d081909227488e480a69a5 completed May 19, 2026, 2:25 a.m.
NEDg Description generation batch_6a0bcca3edf081908254a31bb0f5576e completed May 19, 2026, 2:36 a.m.
NED2 Entity disambiguation (via description) batch_6a0bcd53eecc81909ac4ec82da07bc6b completed May 19, 2026, 2:39 a.m.
Created at: April 17, 2026, 3:46 p.m.