Triple

T3911814
Position Surface form Disambiguated ID Type / Status
Subject My Blue Heaven E87338 entity
Predicate director P255 FINISHED
Object Henry Koster E56821 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: Henry Koster | Statement: [My Blue Heaven, director, Henry Koster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Henry Koster
Context triple: [My Blue Heaven, director, Henry Koster]
  • A. Henry Koster chosen
    Henry Koster was a German-born American film director best known for his work in Hollywood during the 1930s–1950s, including popular comedies, dramas, and family films.
  • B. Walter Scheib
    Walter Scheib was an American chef best known for serving as the White House Executive Chef for the Clinton and George W. Bush administrations.
  • C. Milt Schaffer
    Milt Schaffer was an American animator and story artist best known for his work on classic Disney cartoons in the mid-20th century.
  • D. Harold Hecht
    Harold Hecht was an American film producer and talent agent best known for co-founding Hecht-Hill-Lancaster and producing acclaimed mid-20th-century films such as "Marty."
  • E. Henry Kolker
    Henry Kolker was an American stage and film actor and director active in the early 20th century, known for his character roles in both silent and sound films.
  • 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_69aed9424514819086e9c58adde6652d completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeed35e2d081908b5d87c7630e7ffc completed March 9, 2026, 3:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51cb454c48190bf47d080f6cc24f0 completed March 14, 2026, 8:30 a.m.
Created at: March 9, 2026, 3:22 p.m.