Triple

T2321022
Position Surface form Disambiguated ID Type / Status
Subject PEP 572 E51179 entity
Predicate author P4 FINISHED
Object Chris Angelico
Chris Angelico is a Python developer and community contributor known for his involvement in Python Enhancement Proposals, including co-authoring PEP 572.
E256162 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: Chris Angelico | Statement: [PEP 572, author, Chris Angelico]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chris Angelico
Context triple: [PEP 572, author, Chris Angelico]
  • A. Matthew C. Brown
    Matthew C. Brown is a film producer known for his work on the horror movie "Spectral."
  • B. Eric Evans
    Eric Evans is a software engineer and thought leader best known for originating and popularizing the concept of Domain-Driven Design in enterprise software development.
  • C. Mark Lamping
    Mark Lamping is an American sports executive known for leading the business operations of major professional teams, including serving as president of the NFL’s Jacksonville Jaguars.
  • D. Robert Kern
    Robert Kern was an American film editor active during Hollywood’s classic studio era, known for his work on numerous prominent MGM productions.
  • E. Trevor Perrin
    Trevor Perrin is a cryptographer and software engineer best known for creating the Noise protocol framework and co-designing the Signal Protocol used in secure messaging.
  • 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: Chris Angelico
Triple: [PEP 572, author, Chris Angelico]
Generated description
Chris Angelico is a Python developer and community contributor known for his involvement in Python Enhancement Proposals, including co-authoring PEP 572.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Chris Angelico
Target entity description: Chris Angelico is a Python developer and community contributor known for his involvement in Python Enhancement Proposals, including co-authoring PEP 572.
  • A. Matthew C. Brown
    Matthew C. Brown is a film producer known for his work on the horror movie "Spectral."
  • B. Eric Evans
    Eric Evans is a software engineer and thought leader best known for originating and popularizing the concept of Domain-Driven Design in enterprise software development.
  • C. Mark Lamping
    Mark Lamping is an American sports executive known for leading the business operations of major professional teams, including serving as president of the NFL’s Jacksonville Jaguars.
  • D. Robert Kern
    Robert Kern was an American film editor active during Hollywood’s classic studio era, known for his work on numerous prominent MGM productions.
  • E. Trevor Perrin
    Trevor Perrin is a cryptographer and software engineer best known for creating the Noise protocol framework and co-designing the Signal Protocol used in secure messaging.
  • 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_69a88b074b908190ae983dbca7757d88 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc632474c8190972b4611a3a4ff8f completed March 7, 2026, 6:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae896911908190b53954dbf854cc18 completed March 9, 2026, 8:48 a.m.
NEDg Description generation batch_69ae8d2dcc8081908d4274b2287ff2b8 completed March 9, 2026, 9:04 a.m.
NED2 Entity disambiguation (via description) batch_69ae8d786a648190acf0a14e0d4a120c completed March 9, 2026, 9:06 a.m.
Created at: March 4, 2026, 7:49 p.m.