Triple

T680714
Position Surface form Disambiguated ID Type / Status
Subject Ernest B. Schoedsack E13173 entity
Predicate notableWork P4 FINISHED
Object Grass E82335 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: Grass | Statement: [Ernest B. Schoedsack, notableWork, Grass]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grass
Context triple: [Ernest B. Schoedsack, notableWork, Grass]
  • A. Grass chosen
    Grass is a 1925 silent documentary film that follows the arduous seasonal migration of the Bakhtiari tribe in Iran, co-directed by Merian C. Cooper and Ernest B. Schoedsack.
  • B. Lucerne
    Lucerne is a picturesque Swiss city known for its preserved medieval architecture, lakeside setting on Lake Lucerne, and proximity to the Swiss Alps.
  • C. Moss
    Moss is a coastal town and municipality in southeastern Norway known for its industrial history, cultural life, and role as a regional hub in Østfold.
  • D. Moss
    Moss is a masculine given name most notably borne by American playwright and director Moss Hart.
  • E. Kikuyu
    Kikuyu is a major Bantu language spoken primarily by the Kikuyu people of central Kenya.
  • 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_69a4933d3bf88190972041cd8cf143b9 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4a06e294c8190873116a3253e04f9 completed March 1, 2026, 8:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5dca153e081908facd835a79da25d completed March 2, 2026, 6:53 p.m.
Created at: March 1, 2026, 7:36 p.m.