Triple

T14702819
Position Surface form Disambiguated ID Type / Status
Subject Si E345348 entity
Predicate appearsWith P4540 FINISHED
Object Darling
Darling is a character known for appearing alongside Si, likely within a shared fictional or entertainment context.
E1118185 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: Darling | Statement: [Si, appearsWith, Darling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Darling
Context triple: [Si, appearsWith, Darling]
  • A. Darling
    Darling is a 1965 British drama film directed by John Schlesinger, known for its incisive portrayal of a young woman's rise in London's high society and for winning multiple Academy Awards.
  • B. Darling
    Darling is a residential suburb in Melbourne, Victoria, known for its local train station on the Glen Waverley railway line and its proximity to the city.
  • C. Darling
    Darling is a surname most prominently associated with Ron Darling, a former Major League Baseball pitcher and current television baseball analyst.
  • D. Darling
    Darling is the kind, affectionate human owner of Lady in Disney's animated film "Lady and the Tramp."
  • E. Darling
    Darling is a South African wine-producing district known for its cool coastal climate and quality white and red wines, particularly Sauvignon Blanc.
  • 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: Darling
Triple: [Si, appearsWith, Darling]
Generated description
Darling is a character known for appearing alongside Si, likely within a shared fictional or entertainment context.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Darling
Target entity description: Darling is a character known for appearing alongside Si, likely within a shared fictional or entertainment context.
  • A. Darling
    Darling is a character played by Eiza González in the action film "Baby Driver," known as a stylish and dangerous bank robber and the girlfriend of fellow criminal Buddy.
  • B. Darling
    Darling is the kind, affectionate human owner of Lady in Disney's animated film "Lady and the Tramp."
  • C. Darling
    "Darling" is a 2010 Telugu romantic comedy film starring Prabhas, known for its lighthearted love story and popular music.
  • D. Darling
    Darling is a surname most prominently associated with Ron Darling, a former Major League Baseball pitcher and current television baseball analyst.
  • E. Darling
    Darling is a residential suburb in Melbourne, Victoria, known for its local train station on the Glen Waverley railway line and its proximity to the city.
  • 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_69d822e4a8c08190a155df736bb7bc13 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb6071e5c8190bb5509c859135c2d completed April 14, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe0cdfb09481908021a3fc92962a00 completed May 8, 2026, 4:18 p.m.
NEDg Description generation batch_69fe1256241881909b62655a5e1d9332 completed May 8, 2026, 4:41 p.m.
NED2 Entity disambiguation (via description) batch_69fe1347c6c48190b1681ac5f5308f19 completed May 8, 2026, 4:45 p.m.
Created at: April 10, 2026, 1:28 a.m.