Triple

T926510
Position Surface form Disambiguated ID Type / Status
Subject Kingsley Wood E19994 entity
Predicate familyName P18 FINISHED
Object Wood
Wood is a common English surname with historical roots in Britain, often originally referring to someone who lived or worked near a forest.
 E109794 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: Wood | Statement: [Kingsley Wood, familyName, Wood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wood
Context triple: [Kingsley Wood, familyName, Wood]
  • A. Clinch Leatherwood
    Clinch Leatherwood is the ruthless outlaw gunslinger who serves as the main antagonist in the comedy Western film "A Million Ways to Die in the West."
  • B. Lignum vitae
    Lignum vitae is a dense, extremely hard tropical hardwood tree native to the Caribbean, renowned for its durable wood and medicinal resin.
  • C. Douglas fir
    Douglas fir is a large, long-lived conifer native to western North America, valued for its strong timber and ecological importance in mountain and coastal forests.
  • D. USOAK
    USOAK is the UN/LOCODE identifier for the Port of Oakland, a major container shipping hub on the U.S. West Coast.
  • E. Woods
    Woods is a common English surname of Anglo-Saxon origin, typically referring to someone who lived or worked in or near a forest.
  • 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: Wood
Triple: [Kingsley Wood, familyName, Wood]
Generated description
Wood is a common English surname with historical roots in Britain, often originally referring to someone who lived or worked near a forest.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wood
Target entity description: Wood is a common English surname with historical roots in Britain, often originally referring to someone who lived or worked near a forest.
  • A. Clinch Leatherwood
    Clinch Leatherwood is the ruthless outlaw gunslinger who serves as the main antagonist in the comedy Western film "A Million Ways to Die in the West."
  • B. Lignum vitae
    Lignum vitae is a dense, extremely hard tropical hardwood tree native to the Caribbean, renowned for its durable wood and medicinal resin.
  • C. Douglas fir
    Douglas fir is a large, long-lived conifer native to western North America, valued for its strong timber and ecological importance in mountain and coastal forests.
  • D. USOAK
    USOAK is the UN/LOCODE identifier for the Port of Oakland, a major container shipping hub on the U.S. West Coast.
  • E. Woods
    Woods is a common English surname of Anglo-Saxon origin, typically referring to someone who lived or worked in or near a forest.
  • 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_69a493a099788190a696d9d8408cbaf4 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b32c5b508190a570c94a6647ba70 completed March 1, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7ee0becc0819089b3dadc618cf83c completed March 4, 2026, 8:32 a.m.
NEDg Description generation batch_69a7f3607c70819080c5e3e49ccc6de5 completed March 4, 2026, 8:54 a.m.
NED2 Entity disambiguation (via description) batch_69a7f3d1e97881909cbb97a8ca89c574 completed March 4, 2026, 8:56 a.m.
Created at: March 1, 2026, 7:40 p.m.