Triple

T9312860
Position Surface form Disambiguated ID Type / Status
Subject RFC 7720 E224045 entity
Predicate author P4 FINISHED
Object Matt Larson
Matt Larson is an Internet engineer and author known for his work on DNS standards and contributions to key IETF documents.
E791340 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: Matt Larson | Statement: [RFC 7720, author, Matt Larson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matt Larson
Context triple: [RFC 7720, author, Matt Larson]
  • A. Dean Paul Larson
    Dean Paul Larson is a fictional character from the television series "The Chair."
  • B. Michael Larsen
    Michael Larsen is the person credited with coining the now-popular term “Painted Ladies” to describe the colorfully restored Victorian and Edwardian houses of San Francisco.
  • C. Nathan Larson
    Nathan Larson is an American musician and film composer known for scoring numerous independent and mainstream movies.
  • D. Michael Nolin
    Michael Nolin is an American film producer best known for his work on the acclaimed music drama "Mr. Holland's Opus."
  • E. Keith Larsen
    Keith Larsen was an American actor best known for his roles in 1950s Western films and television 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: Matt Larson
Triple: [RFC 7720, author, Matt Larson]
Generated description
Matt Larson is an Internet engineer and author known for his work on DNS standards and contributions to key IETF documents.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Matt Larson
Target entity description: Matt Larson is an Internet engineer and author known for his work on DNS standards and contributions to key IETF documents.
  • A. Dean Paul Larson
    Dean Paul Larson is a fictional character from the television series "The Chair."
  • B. Michael Larsen
    Michael Larsen is the person credited with coining the now-popular term “Painted Ladies” to describe the colorfully restored Victorian and Edwardian houses of San Francisco.
  • C. Nathan Larson
    Nathan Larson is an American musician and film composer known for scoring numerous independent and mainstream movies.
  • D. Michael Nolin
    Michael Nolin is an American film producer best known for his work on the acclaimed music drama "Mr. Holland's Opus."
  • E. Keith Larsen
    Keith Larsen was an American actor best known for his roles in 1950s Western films and television 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_69ca8425f4fc81909c1c586e9a5b7530 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd20ae96e481909a1af9ea1c91f2b2 completed April 1, 2026, 1:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0c7a0adc4819097ac906f03f0188e completed April 4, 2026, 8:11 a.m.
NEDg Description generation batch_69d0c8a7190c819097e71c15f7924268 completed April 4, 2026, 8:15 a.m.
NED2 Entity disambiguation (via description) batch_69d0c9e7e7d08190bddc6786f0fcea9e completed April 4, 2026, 8:20 a.m.
Created at: March 30, 2026, 7:37 p.m.