Triple

T10956114
Position Surface form Disambiguated ID Type / Status
Subject Handy Man E258850 entity
Predicate includedInAlbum P1925 FINISHED
Object JT E258854 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: JT | Statement: [Handy Man, includedInAlbum, JT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: JT
Context triple: [Handy Man, includedInAlbum, JT]
  • A. JT chosen
    JT is a 1977 studio album by American singer-songwriter James Taylor that marked his commercial resurgence with hits like "Handy Man" and "Your Smiling Face."
  • B. JT
    JT is a lightweight 3D visualization and data exchange file format commonly used in CAD and PLM workflows for efficient sharing of complex product models.
  • C. JK
    JK is the widely used nickname of Juscelino Kubitschek, the former president of Brazil best known for founding Brasília and promoting rapid national development.
  • D. JR
    JR is a French street artist and photographer renowned for his large-scale public art installations that transform urban spaces and address social and political issues worldwide.
  • E. JR
    JR is a character from Alison Bechdel’s long-running comic strip "Dykes to Watch Out For," which chronicles the lives and relationships of a diverse group of lesbian friends.
  • 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_69d6aa88500c819097d7032ca578e74f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7710088e8819099e3272e26566c44 completed April 9, 2026, 9:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69e23c72196c8190b2336a130b64ab8e completed April 17, 2026, 1:58 p.m.
Created at: April 8, 2026, 9:23 p.m.