Triple
T10940404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shirley Marlin Noznisky |
E258453
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Samuel Dylan |
E490548
|
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: Samuel Dylan | Statement: [Shirley Marlin Noznisky, hasChild, Samuel Dylan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samuel Dylan Context triple: [Shirley Marlin Noznisky, hasChild, Samuel Dylan]
-
A.
Samuel Dylan
chosen
Samuel Dylan is one of Bob Dylan and Sara Lowndes's children, known primarily for his connection to the iconic musician's family.
-
B.
Sir Dylan
Sir Dylan is a music producer best known for his work on Miguel’s album "War & Leisure."
-
C.
Dylan
Dylan is a masculine given name of Welsh origin, widely used in English-speaking countries.
-
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 (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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770c2821c8190a7b08276c4bfbf33 |
completed | April 9, 2026, 9:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e23c0e940081908c84ea4cf3b877fc |
completed | April 17, 2026, 1:56 p.m. |
Created at: April 8, 2026, 9:23 p.m.