Triple

T9279692
Position Surface form Disambiguated ID Type / Status
Subject Jenna Fischer E223036 entity
Predicate givenName P17 FINISHED
Object Regina
Regina is the given first name of American actress Jenna Fischer, best known for her role as Pam Beesly on the U.S. version of "The Office."
 E788074 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: Regina | Statement: [Jenna Fischer, givenName, Regina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Regina
Context triple: [Jenna Fischer, givenName, Regina]
  • A. Regina
    Regina is a fictional character known for her role as a maid.
  • B. Regina
    Regina is a 1949 opera by American composer Marc Blitzstein, adapted from Lillian Hellman’s play "The Little Foxes."
  • C. Regina, Saskatchewan, Canada
    Regina, Saskatchewan, Canada is the capital city of the province of Saskatchewan, known as a major cultural and economic center on the Canadian Prairies.
  • D. Regina metropolitan area
    The Regina metropolitan area is the urban region centered on Regina, the capital city of Saskatchewan, Canada, encompassing the city and its surrounding communities.
  • E. Red Deer
    Red Deer is a mid-sized Canadian city in central Alberta known as a regional hub for agriculture, industry, and commerce between Calgary and Edmonton.
  • 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: Regina
Triple: [Jenna Fischer, givenName, Regina]
Generated description
Regina is the given first name of American actress Jenna Fischer, best known for her role as Pam Beesly on the U.S. version of "The Office."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Regina
Target entity description: Regina is the given first name of American actress Jenna Fischer, best known for her role as Pam Beesly on the U.S. version of "The Office."
  • A. Regina
    Regina is a fictional character known for her role as a maid.
  • B. Regina
    Regina is a 1949 opera by American composer Marc Blitzstein, adapted from Lillian Hellman’s play "The Little Foxes."
  • C. Regina, Saskatchewan, Canada
    Regina, Saskatchewan, Canada is the capital city of the province of Saskatchewan, known as a major cultural and economic center on the Canadian Prairies.
  • D. Regina metropolitan area
    The Regina metropolitan area is the urban region centered on Regina, the capital city of Saskatchewan, Canada, encompassing the city and its surrounding communities.
  • E. Red Deer
    Red Deer is a mid-sized Canadian city in central Alberta known as a regional hub for agriculture, industry, and commerce between Calgary and Edmonton.
  • 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_69ca842123588190b3f2e1a69037d141 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd07cc79508190954defbef0d82a64 completed April 1, 2026, 11:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69d09c4f254c819083301449c3e4b11d completed April 4, 2026, 5:06 a.m.
NEDg Description generation batch_69d09da280288190b40f145e1836a53f completed April 4, 2026, 5:12 a.m.
NED2 Entity disambiguation (via description) batch_69d09df7c7f48190b36b63227ff539b5 completed April 4, 2026, 5:13 a.m.
Created at: March 30, 2026, 7:34 p.m.