Triple
T405502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Maynard |
E9374
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Donald |
E14147
|
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: Donald | Statement: [Don Maynard, givenName, Donald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donald Context triple: [Don Maynard, givenName, Donald]
-
A.
Donald
chosen
Donald is the given name of Donald Trump, the 45th president of the United States and a prominent businessman and media personality.
-
B.
Don
The Don is a major river in southwestern Russia that flows from the Central Russian Upland to the Sea of Azov, historically serving as an important trade route and cultural boundary.
-
C.
Ronald
Ronald is the given first name of American filmmaker and former child actor Ron Howard.
-
D.
John
John is the given name of John Hancock, a prominent American statesman and patriot best known for his large signature on the United States Declaration of Independence.
-
E.
John
John is the given name of John Bardeen, the American physicist who uniquely won the Nobel Prize in Physics twice for his work on the transistor and superconductivity.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eca37fe881909802126952dfdd59 |
completed | Feb. 28, 2026, 1:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a413f594848190bc73e37f30684a37 |
completed | March 1, 2026, 10:24 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.