Triple

T7836848
Position Surface form Disambiguated ID Type / Status
Subject George Clymer E181708 entity
Predicate fatherInLaw P18081 FINISHED
Object Reese Meredith
Reese Meredith was an 18th-century Philadelphia merchant and political figure connected to early American revolutionary circles.
E698789 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: Reese Meredith | Statement: [George Clymer, fatherInLaw, Reese Meredith]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reese Meredith
Context triple: [George Clymer, fatherInLaw, Reese Meredith]
  • A. Katie Lee
    Katie Lee is an American cookbook author, food critic, and television personality known for her culinary work and for having been married to musician Billy Joel.
  • B. Dani Reese
    Dani Reese is a fictional Los Angeles police detective and main character from the TV series "Life," portrayed by actress Sarah Shahi.
  • C. Savannah Guthrie
    Savannah Guthrie is an American broadcast journalist and attorney best known as a longtime co-anchor of NBC’s morning show "Today."
  • D. Caroline Nantz
    Caroline Nantz is the daughter of renowned American sportscaster Jim Nantz.
  • E. Rose Morgan
    Rose Morgan is the introspective, insecure Columbia University professor who undergoes a journey of self-discovery and romantic awakening in the film "The Mirror Has Two Faces."
  • 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: Reese Meredith
Triple: [George Clymer, fatherInLaw, Reese Meredith]
Generated description
Reese Meredith was an 18th-century Philadelphia merchant and political figure connected to early American revolutionary circles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Reese Meredith
Target entity description: Reese Meredith was an 18th-century Philadelphia merchant and political figure connected to early American revolutionary circles.
  • A. Katie Lee
    Katie Lee is an American cookbook author, food critic, and television personality known for her culinary work and for having been married to musician Billy Joel.
  • B. Dani Reese
    Dani Reese is a fictional Los Angeles police detective and main character from the TV series "Life," portrayed by actress Sarah Shahi.
  • C. Savannah Guthrie
    Savannah Guthrie is an American broadcast journalist and attorney best known as a longtime co-anchor of NBC’s morning show "Today."
  • D. Caroline Nantz
    Caroline Nantz is the daughter of renowned American sportscaster Jim Nantz.
  • E. Rose Morgan
    Rose Morgan is the introspective, insecure Columbia University professor who undergoes a journey of self-discovery and romantic awakening in the film "The Mirror Has Two Faces."
  • 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_69ca8284a25c8190a1a20afad30da792 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb14c203b4819099c039c617628927 completed March 31, 2026, 12:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5aad325c8190bced57e8380bc729 completed March 31, 2026, 5:25 a.m.
NEDg Description generation batch_69cb762dd8348190bf74be4e7f5df1e7 completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbb24068908190977b266366e5ceea completed March 31, 2026, 11:38 a.m.
Created at: March 30, 2026, 4:46 p.m.