Triple

T2769276
Position Surface form Disambiguated ID Type / Status
Subject Henry Harwood E61413 entity
Predicate familyName P18 FINISHED
Object Harwood
Harwood is an English-language surname borne by various notable individuals across fields such as the military, arts, and politics.
 E298496 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: Harwood | Statement: [Henry Harwood, familyName, Harwood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Harwood
Context triple: [Henry Harwood, familyName, Harwood]
  • A. Linwood
    Linwood is a small Scottish town in Renfrewshire, near Paisley, known historically for its car manufacturing and as a residential commuter community for the Greater Glasgow area.
  • B. 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.
  • C. Upland
    Upland is a suburban city in Southern California’s Inland Empire, located at the foot of the San Gabriel Mountains.
  • D. Halstead
    Halstead is a historic market town in the county of Essex in the East of England.
  • E. Hadleyville
    Hadleyville is the fictional small Western town in the classic 1952 film "High Noon," where the story’s tense showdown unfolds.
  • 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: Harwood
Triple: [Henry Harwood, familyName, Harwood]
Generated description
Harwood is an English-language surname borne by various notable individuals across fields such as the military, arts, and politics.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Harwood
Target entity description: Harwood is an English-language surname borne by various notable individuals across fields such as the military, arts, and politics.
  • A. Linwood
    Linwood is a small Scottish town in Renfrewshire, near Paisley, known historically for its car manufacturing and as a residential commuter community for the Greater Glasgow area.
  • B. 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.
  • C. Upland
    Upland is a suburban city in Southern California’s Inland Empire, located at the foot of the San Gabriel Mountains.
  • D. Halstead
    Halstead is a historic market town in the county of Essex in the East of England.
  • E. Hadleyville
    Hadleyville is the fictional small Western town in the classic 1952 film "High Noon," where the story’s tense showdown unfolds.
  • 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_69ab4b7cd13481909174bca9809ed259 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdd6785d88190b99f99889463a962 completed March 7, 2026, 8:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6496dc88190b316d5b36bc5df67 completed March 10, 2026, 7:20 a.m.
NEDg Description generation batch_69afc6e6d4bc81908108fe24677448c3 completed March 10, 2026, 7:23 a.m.
NED2 Entity disambiguation (via description) batch_69afc7907be88190b70458ed735261e8 completed March 10, 2026, 7:26 a.m.
Created at: March 6, 2026, 9:57 p.m.