Triple

T12146062
Position Surface form Disambiguated ID Type / Status
Subject Phyllis Haver E289324 entity
Predicate givenName P17 FINISHED
Object Phyllis
Phyllis is a feminine given name that gained popularity in English-speaking countries, particularly in the early to mid-20th century.
 E318455 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: Phyllis | Statement: [Phyllis Haver, givenName, Phyllis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Phyllis
Context triple: [Phyllis Haver, givenName, Phyllis]
  • A. Phyllis
    Phyllis is a tragic figure from classical legend, often depicted as a wronged lover who is transformed into an almond tree after being abandoned.
  • B. Phyllis
    Phyllis is a 1970s American television sitcom, spun off from The Mary Tyler Moore Show, that stars Cloris Leachman as the widowed Phyllis Lindstrom starting a new life in San Francisco.
  • C. Ethel
    Ethel is a feminine given name of Old English origin, historically popular in English-speaking countries.
  • D. Phylicia
    Phylicia is a feminine given name best known through American actress and director Phylicia Rashad.
  • E. Barbara
    Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
  • 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: Phyllis
Triple: [Phyllis Haver, givenName, Phyllis]
Generated description
Phyllis is a feminine given name that gained popularity in English-speaking countries, particularly in the early to mid-20th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Phyllis
Target entity description: Phyllis is a feminine given name that gained popularity in English-speaking countries, particularly in the early to mid-20th century.
  • A. Phyllis
    Phyllis is a 1970s American television sitcom, spun off from The Mary Tyler Moore Show, that stars Cloris Leachman as the widowed Phyllis Lindstrom starting a new life in San Francisco.
  • B. Phyllis chosen
    Phyllis is a tragic figure from classical legend, often depicted as a wronged lover who is transformed into an almond tree after being abandoned.
  • C. Ethel
    Ethel is a feminine given name of Old English origin, historically popular in English-speaking countries.
  • D. Phylicia
    Phylicia is a feminine given name best known through American actress and director Phylicia Rashad.
  • E. Barbara
    Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
  • F. None of above.

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_69d6ab4c6710819097a9d228382dde43 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915ac2ebc81909155f9b2fb4a2252 completed April 10, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6717defe48190bc63b83e2c276ebc completed May 2, 2026, 9:49 p.m.
NEDg Description generation batch_69f6749c2ddc8190945270e6e5b210dd completed May 2, 2026, 10:03 p.m.
NED2 Entity disambiguation (via description) batch_69f675c42fec8190b60751c0db88f3b6 completed May 2, 2026, 10:08 p.m.
Created at: April 8, 2026, 9:49 p.m.