Triple
T12589215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skip Young |
E300555
|
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: [Skip Young, hasGivenName, Ronald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ronald Context triple: [Skip Young, 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 name of American country music singer-songwriter Don Gibson, known for hits like "Oh Lonesome Me" and "I Can't Stop Loving You."
-
E.
Donald
Donald is the given name of Donald David Coryell, the innovative American football coach known for revolutionizing the passing game in the NFL.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954bd5e8c8190a2f233b91682341f |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ec0a60c8190948706e8b2fcc0ad |
completed | May 2, 2026, 8:29 p.m. |
Created at: April 9, 2026, 5:06 p.m.