Triple

T5080669
Position Surface form Disambiguated ID Type / Status
Subject Sophie Milman E114501 entity
Predicate notableWork P4 FINISHED
Object Take Love Easy E493056 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: Take Love Easy | Statement: [Sophie Milman, notableWork, Take Love Easy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Take Love Easy
Context triple: [Sophie Milman, notableWork, Take Love Easy]
  • A. Take Love Easy chosen
    "Take Love Easy" is a jazz album by vocalist Sophie Milman, showcasing her smooth, expressive interpretations of classic and contemporary standards.
  • B. Easy to Love
    "Easy to Love" is a popular Cole Porter standard that has been widely recorded in jazz and pop, including by Ella Fitzgerald on her celebrated Cole Porter songbook album.
  • C. Here’s Love
    "Here’s Love" is a 1963 Broadway musical by Meredith Willson, adapted from the classic film "Miracle on 34th Street."
  • D. Let Love
    Let Love is a 2019 R&B and soul album by American rapper and actor Common that explores themes of spirituality, personal growth, and emotional vulnerability.
  • E. Ready to Love
    Ready to Love is a reality dating series that follows successful Black singles navigating romance and relationships, produced by filmmaker and TV producer Will Packer.
  • 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_69bd443dbf908190a9401e9c2dc7bd7d completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd74f9d9848190919aad6cfe14f1cf completed March 20, 2026, 4:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69beba6f3eb48190b544bf743829cb0c completed March 21, 2026, 3:34 p.m.
Created at: March 20, 2026, 1:39 p.m.