Triple
T20464018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dylan family |
E502001
|
entity |
| Predicate | hasNotableMember |
P304
|
FINISHED |
| Object | Bob Dylan |
—
|
NE NERFINISHED |
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: Bob Dylan | Statement: [Dylan family, hasNotableMember, Bob Dylan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bob Dylan Context triple: [Dylan family, hasNotableMember, Bob Dylan]
-
A.
Bob Dylan
chosen
Bob Dylan is an influential American singer-songwriter and cultural icon whose poetic lyrics and groundbreaking work in popular music earned him the Nobel Prize in Literature.
-
B.
Sir Dylan
Sir Dylan is a music producer best known for his work on Miguel’s album "War & Leisure."
-
C.
Leonard Cohen
Leonard Cohen was a Canadian singer-songwriter, poet, and novelist renowned for his deep, poetic lyrics and melancholic songs such as "Hallelujah" and "Suzanne."
-
D.
Dylan
Dylan is a multi-paradigm programming language designed for dynamic, object-oriented application development, known for combining Lisp-like semantics with a more conventional, infix syntax.
-
E.
Dylan
Dylan is a play by Sidney Michaels that dramatizes the life and work of Welsh poet Dylan Thomas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ad4940819098cf2ff6413574e5 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e696a88374819087e7b97c83074d2c |
completed | April 20, 2026, 9:12 p.m. |
Created at: April 16, 2026, 11:33 a.m.