Triple
T10493552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ronald Howard |
E247476
|
entity |
| Predicate | hasGivenName |
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: [Ronald Howard, hasGivenName, Ronald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ronald Context triple: [Ronald Howard, hasGivenName, Ronald]
-
A.
Ronald
chosen
Ronald is the given first name of American filmmaker and former child actor Ron Howard.
-
B.
Roone
Roone is a masculine given name most notably associated with pioneering American television sports and news executive Roone Arledge.
-
C.
Donald
Donald is a fictional character portrayed by American actor Matt Bomer, likely in a film or television production.
-
D.
Donald
Donald is the given first name of influential American jazz musician, arranger, and bandleader Don Redman.
-
E.
Donald
Donald is the given first name of American singer and actor Donnie Wahlberg, a member of New Kids on the Block.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5097fe2bc81909d66ce43f3533284 |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dcaeb6088190829b6c26eb1de7d5 |
completed | April 10, 2026, 11:19 a.m. |
Created at: April 6, 2026, 12:24 p.m.