Triple

T2471029
Position Surface form Disambiguated ID Type / Status
Subject Peter Townsend E55372 entity
Predicate familyName P18 FINISHED
Object Townsend
Townsend is a surname of English origin borne by numerous notable individuals across fields such as politics, science, and the arts.
 E270517 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: Townsend | Statement: [Peter Townsend, familyName, Townsend]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Townsend
Context triple: [Peter Townsend, familyName, Townsend]
  • A. Tilton
    Tilton is a locality in the United Kingdom notable for lending its name to the territorial designation of the peerage title Baron Keynes of Tilton.
  • B. Winslow
    Winslow is the main commercial and residential hub of Bainbridge Island, Washington, known for its downtown shops, restaurants, and ferry terminal connecting to Seattle.
  • C. Winslow
    Winslow is an English-origin surname historically associated with early colonial families in New England.
  • D. Upland
    Upland is a suburban city in Southern California’s Inland Empire, located at the foot of the San Gabriel Mountains.
  • E. Upland
    Upland is a small borough in Delaware County, Pennsylvania, known historically as the original name and early settlement area that later became part of Chester.
  • 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: Townsend
Triple: [Peter Townsend, familyName, Townsend]
Generated description
Townsend is a surname of English origin borne by numerous notable individuals across fields such as politics, science, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Townsend
Target entity description: Townsend is a surname of English origin borne by numerous notable individuals across fields such as politics, science, and the arts.
  • A. Tilton
    Tilton is a locality in the United Kingdom notable for lending its name to the territorial designation of the peerage title Baron Keynes of Tilton.
  • B. Winslow
    Winslow is the main commercial and residential hub of Bainbridge Island, Washington, known for its downtown shops, restaurants, and ferry terminal connecting to Seattle.
  • C. Winslow
    Winslow is an English-origin surname historically associated with early colonial families in New England.
  • D. Upland
    Upland is a suburban city in Southern California’s Inland Empire, located at the foot of the San Gabriel Mountains.
  • E. Upland
    Upland is a small borough in Delaware County, Pennsylvania, known historically as the original name and early settlement area that later became part of Chester.
  • 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_69ab49e3622c8190ad22afa2c4fbb807 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd136f5388190801d0b9dc66ad36f completed March 7, 2026, 7:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69af17a5bb04819090b3156a9819b87d completed March 9, 2026, 6:55 p.m.
NEDg Description generation batch_69af1aca9a5081909e3a1b810b61e19d completed March 9, 2026, 7:08 p.m.
NED2 Entity disambiguation (via description) batch_69af1b45a30c8190a3555fea9c03e343 completed March 9, 2026, 7:11 p.m.
Created at: March 6, 2026, 9:44 p.m.