Triple

T718579
Position Surface form Disambiguated ID Type / Status
Subject Harry Dexter White E14364 entity
Predicate familyName P18 FINISHED
Object White
White is a common English surname borne by numerous notable individuals across politics, arts, sciences, and other fields.
 E86909 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: White | Statement: [Harry Dexter White, familyName, White]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: White
Context triple: [Harry Dexter White, familyName, White]
  • A. Blue
    Blue is the anthropomorphic blue horse who serves as the official mascot of the NFL’s Indianapolis Colts.
  • B. Blue
    Blue is a critically acclaimed 1971 folk album by Joni Mitchell, widely regarded as one of the greatest and most influential records in popular music history.
  • C. Blanc
    Blanc is the surname of Mel Blanc, the legendary American voice actor best known for bringing to life many iconic Looney Tunes characters.
  • D. Whitney
    Whitney is a common English surname most famously associated with Eli Whitney, the American inventor of the cotton gin.
  • E. Stripes
    Stripes is a 1981 American comedy film starring Bill Murray as a slacker who impulsively joins the U.S. Army, leading to a series of irreverent misadventures.
  • 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: White
Triple: [Harry Dexter White, familyName, White]
Generated description
White is a common English surname borne by numerous notable individuals across politics, arts, sciences, and other fields.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: White
Target entity description: White is a common English surname borne by numerous notable individuals across politics, arts, sciences, and other fields.
  • A. Blue
    Blue is the anthropomorphic blue horse who serves as the official mascot of the NFL’s Indianapolis Colts.
  • B. Blue
    Blue is a critically acclaimed 1971 folk album by Joni Mitchell, widely regarded as one of the greatest and most influential records in popular music history.
  • C. Blanc
    Blanc is the surname of Mel Blanc, the legendary American voice actor best known for bringing to life many iconic Looney Tunes characters.
  • D. Whitney
    Whitney is a common English surname most famously associated with Eli Whitney, the American inventor of the cotton gin.
  • E. Stripes
    Stripes is a 1981 American comedy film starring Bill Murray as a slacker who impulsively joins the U.S. Army, leading to a series of irreverent misadventures.
  • 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_69a4934a36e081909e7abef98b898a4e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a58d4c3c8190ad4527d14bca5e6e completed March 1, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a63759d6108190adcdeac45e4c7766 completed March 3, 2026, 1:20 a.m.
NEDg Description generation batch_69a63b158dc881909da0b90f498e9a43 completed March 3, 2026, 1:36 a.m.
NED2 Entity disambiguation (via description) batch_69a63eccc5a08190b39b7818dc61591c completed March 3, 2026, 1:52 a.m.
Created at: March 1, 2026, 7:37 p.m.