Triple
T14184765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dilan |
E351544
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Dylan |
E468864
|
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: Dylan | Statement: [Dilan, hasVariant, Dylan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dylan Context triple: [Dilan, hasVariant, Dylan]
-
A.
Dylan
Dylan is one of the children of Tanzanian Bongo Flava star and music entrepreneur Diamond Platnumz.
-
B.
Dylan
Dylan is a surname most famously associated with American singer-songwriter Bob Dylan and his artistic family.
-
C.
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.
-
D.
Dylan
Dylan is a play by Sidney Michaels that dramatizes the life and work of Welsh poet Dylan Thomas.
-
E.
Dylan
chosen
Dylan is a masculine given name of Welsh origin, widely used in English-speaking countries.
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61cc0a848190b660095972b1223b |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd19413bd08190930767abc2c588d4 |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 1:03 a.m.