Triple

T2682684
Position Surface form Disambiguated ID Type / Status
Subject DiDi Richards E57410 entity
Predicate nickname P55 FINISHED
Object DiDi
DiDi is an American professional basketball player known for her defensive prowess and collegiate success with the Baylor Lady Bears.
 E287385 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: DiDi | Statement: [DiDi Richards, nickname, DiDi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DiDi
Context triple: [DiDi Richards, nickname, DiDi]
  • A. DiDi
    DiDi is a major Chinese ride-hailing and mobility technology company that operates a platform for on-demand transportation and related services.
  • B. DI
    DI is the abbreviation for Defence Intelligence, the United Kingdom’s military intelligence organization responsible for providing strategic and operational intelligence to the government and armed forces.
  • C. DD
    DD is the official vehicle registration code assigned to the Indian union territory of Dadra and Nagar Haveli and Daman and Diu.
  • D. DD
    DD is the stock ticker symbol for DuPont, a major American chemicals and materials company known for innovations such as nylon, Kevlar, and Teflon.
  • E. DD
    DD is the vehicle registration code used on license plates for the German city of Dresden.
  • 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: DiDi
Triple: [DiDi Richards, nickname, DiDi]
Generated description
DiDi is an American professional basketball player known for her defensive prowess and collegiate success with the Baylor Lady Bears.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DiDi
Target entity description: DiDi is an American professional basketball player known for her defensive prowess and collegiate success with the Baylor Lady Bears.
  • A. DiDi
    DiDi is a major Chinese ride-hailing and mobility technology company that operates a platform for on-demand transportation and related services.
  • B. DI
    DI is the abbreviation for Defence Intelligence, the United Kingdom’s military intelligence organization responsible for providing strategic and operational intelligence to the government and armed forces.
  • C. DD
    DD is the official vehicle registration code assigned to the Indian union territory of Dadra and Nagar Haveli and Daman and Diu.
  • D. DD
    DD is the stock ticker symbol for DuPont, a major American chemicals and materials company known for innovations such as nylon, Kevlar, and Teflon.
  • E. DD
    DD is the vehicle registration code used on license plates for the German city of Dresden.
  • 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_69ab4a5028388190a36f3baf1588309e completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd9d602848190b638e417e710a555 completed March 7, 2026, 7:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69afa06c7a908190ae3463bf3e204fa6 completed March 10, 2026, 4:39 a.m.
NEDg Description generation batch_69afa1196aac81909b25557dff5acf5e completed March 10, 2026, 4:42 a.m.
NED2 Entity disambiguation (via description) batch_69afa1a7d9b48190a8b14a7d209e1f26 completed March 10, 2026, 4:44 a.m.
Created at: March 6, 2026, 9:54 p.m.