Triple
T3536003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mac |
E74773
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ronald |
E31235
|
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: Ronald | Statement: [Mac, givenName, Ronald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ronald Context triple: [Mac, givenName, Ronald]
-
A.
Ronald
chosen
Ronald is the given first name of American filmmaker and former child actor Ron Howard.
-
B.
Donald
Donald is the given name of Donald Trump, the 45th president of the United States and a prominent businessman and media personality.
-
C.
Ronald Fish
Ronald Fish is a fictional character in P. G. Wodehouse’s Blandings Castle stories, known as one of Lord Emsworth’s amiable but often troublesome younger relatives.
-
D.
Rodney
Rodney is the middle name of James R. Schlesinger, a prominent American economist and government official who served as U.S. Secretary of Defense and the first Secretary of Energy.
-
E.
Ron
Ron is the commonly used first name of American politician Ron DeSantis, the governor of Florida and a prominent figure in contemporary U.S. conservative politics.
- 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbcc4c1d081908938efb71938e1a3 |
completed | March 8, 2026, 6:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b38bd237e881909df210a42346b572 |
completed | March 13, 2026, 4 a.m. |
Created at: March 8, 2026, 3:20 p.m.