Triple

T13696115
Position Surface form Disambiguated ID Type / Status
Subject Weather or Not E328387 entity
Predicate hasTrack P3284 FINISHED
Object Wonderful World E804509 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: Wonderful World | Statement: [Weather or Not, hasTrack, Wonderful World]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wonderful World
Context triple: [Weather or Not, hasTrack, Wonderful World]
  • A. Wonderful World chosen
    "Wonderful World" is a 1965 pop song made famous by the British beat group Herman's Hermits, known for its catchy melody and lighthearted lyrics.
  • B. Wonderful Wonderful
    "Wonderful Wonderful" is a 2017 studio album by American rock band The Killers that blends arena rock with introspective themes and marked their first release to top the Billboard 200 chart.
  • C. Wonderful, Wonderful
    "Wonderful, Wonderful" is a pop song, notably recorded by Johnny Mathis, recognized for its lush orchestration and romantic lyrics.
  • D. Something Wonderful
    "Something Wonderful" is a popular song from the 1951 Rodgers and Hammerstein musical *The King and I*, known for its lyrical expression of complex, forgiving love.
  • E. Wonderful You
    Wonderful You is a British television drama series created by and starring Richard Lumsden, following the romantic and professional misadventures of a young man in London.
  • 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_69d8076ff62081908a7bd79889edd7a0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc8773f388190b2413b1e05fd5fd7 completed April 12, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69f79453395481909d651cb3a128f23d completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:54 p.m.