Triple

T2481172
Position Surface form Disambiguated ID Type / Status
Subject Frances Appleton E55818 entity
Predicate familyName P18 FINISHED
Object Appleton
Appleton is an English-origin surname borne by various notable individuals across fields such as literature, politics, and science.
 E270280 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: Appleton | Statement: [Frances Appleton, familyName, Appleton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Appleton
Context triple: [Frances Appleton, familyName, Appleton]
  • A. Appleton
    Appleton is a mid-sized city in eastern Wisconsin known for its paper industry heritage, proximity to the Fox River, and role as a regional economic and cultural center.
  • B. Hartland
    Hartland is a given name most notably borne by American theoretical physicist Hartland Snyder, known for his early work on non-commutative geometry in quantum field theory.
  • C. Bayfield
    Bayfield is a residential suburb of the historic town of Chepstow in Monmouthshire, Wales.
  • D. Marshfield
    Marshfield is a coastal town in Plymouth County, Massachusetts, known for its beaches along Cape Cod Bay and its New England seaside character.
  • E. Canton
    Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
  • 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: Appleton
Triple: [Frances Appleton, familyName, Appleton]
Generated description
Appleton is an English-origin surname borne by various notable individuals across fields such as literature, politics, and science.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Appleton
Target entity description: Appleton is an English-origin surname borne by various notable individuals across fields such as literature, politics, and science.
  • A. Appleton
    Appleton is a mid-sized city in eastern Wisconsin known for its paper industry heritage, proximity to the Fox River, and role as a regional economic and cultural center.
  • B. Hartland
    Hartland is a given name most notably borne by American theoretical physicist Hartland Snyder, known for his early work on non-commutative geometry in quantum field theory.
  • C. Bayfield
    Bayfield is a residential suburb of the historic town of Chepstow in Monmouthshire, Wales.
  • D. Marshfield
    Marshfield is a coastal town in Plymouth County, Massachusetts, known for its beaches along Cape Cod Bay and its New England seaside character.
  • E. Canton
    Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
  • 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_69ab49e670a88190b928e08302381710 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd161bf3c8190834502968180e9cf completed March 7, 2026, 7:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69af17b146d881909672e9cd4a501a11 completed March 9, 2026, 6:55 p.m.
NEDg Description generation batch_69af19fa53708190835e51d4bf965c62 completed March 9, 2026, 7:05 p.m.
NED2 Entity disambiguation (via description) batch_69af1a60ce6c81908cc88fc7c94a3ea9 completed March 9, 2026, 7:07 p.m.
Created at: March 6, 2026, 9:45 p.m.