Triple

T5725780
Position Surface form Disambiguated ID Type / Status
Subject Henry Kravis E126259 entity
Predicate associatedWith P37 FINISHED
Object Wall Street E2854 NE FINISHED

How this triple was built (2 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: Wall Street | Statement: [Henry Kravis, associatedWith, Wall Street]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wall Street
Context triple: [Henry Kravis, associatedWith, Wall Street]
  • A. Wall Street chosen
    Wall Street is the historic financial district in Lower Manhattan that serves as a global center for banking, trading, and economic power.
  • B. Wall Street
    Wall Street is a 1987 American drama film directed by Oliver Stone that explores the high-stakes world of corporate finance and greed in New York City.
  • C. 40 Wall Street
    40 Wall Street is a historic neo-Gothic skyscraper in New York City's Financial District, once one of the tallest buildings in the world and later owned and branded by the Trump Organization.
  • D. Liberty Market
    Liberty Market is a popular commercial and shopping area in Lahore, Pakistan, known for its wide range of clothing, jewelry, and food outlets.
  • E. World Financial Center
    The World Financial Center is a prominent commercial complex in New York City known for its office towers, retail spaces, and views overlooking the Hudson River.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c0082f723881908ce8bb13a0c0f8b7 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c025085f508190adf5d540bc8a5b1c completed March 22, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a3211b08190868811db3d5268b1 completed March 22, 2026, 9:08 p.m.
Created at: March 22, 2026, 3:47 p.m.