Triple
T5356101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Siegel |
E102695
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Donald |
E75078
|
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 Siegel, givenName, Donald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donald Context triple: [Don Siegel, givenName, Donald]
-
A.
Donald
Donald is the given name of Donald Trump, the 45th president of the United States and a prominent businessman and media personality.
-
B.
Donald
Donald is the given name of Don Revie, the renowned English football player and manager best known for his successful tenure at Leeds United.
-
C.
Donald
Donald III of Scotland was a late 11th-century King of Scots who briefly ruled following the death of his brother Malcolm III.
-
D.
Donald
Donald is the surname of Aaron Donald, the dominant American football defensive tackle widely regarded as one of the greatest defensive players in NFL history.
-
E.
Don
chosen
Don is a masculine given name, often a short form of Donald, 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_69bd43d8f7248190b64c140734b5c9a8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd862f0ea48190bec78690ab3bee51 |
completed | March 20, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21e2b7b08190aca4c2855ff041de |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:01 p.m.