Triple
T7352754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerry Tarkanian |
E169542
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jerry |
E486416
|
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: Jerry | Statement: [Jerry Tarkanian, givenName, Jerry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jerry Context triple: [Jerry Tarkanian, givenName, Jerry]
-
A.
Jerry
Jerry is one of the two cross-dressing musician protagonists in the classic 1959 comedy film "Some Like It Hot," famously portrayed by Jack Lemmon.
-
B.
Jerry
Jerry is the given name of Jerry Lee Lewis, the influential American rock and roll and country music singer and pianist known for hits like "Great Balls of Fire."
-
C.
Jerry
chosen
Jerry is a masculine given name commonly used in English-speaking countries, often as a diminutive of names like Gerald, Jerome, or Jeremy.
-
D.
Jerry
Jerry is the troubled, isolated protagonist of Edward Albee’s one-act play "The Zoo Story," whose intense encounter with a stranger on a park bench drives the drama’s exploration of alienation and human connection.
-
E.
Jerry
Jerry is the video store clerk protagonist of the comedy film "Be Kind Rewind," known for recreating erased movies with homemade, low-budget remakes.
- 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_69c68a5878888190968ce4d04db8d69f |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f10b4adc81909a5a0eacaf2b1887 |
completed | March 27, 2026, 9:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8276a812c8190891df68bd79d79bd |
completed | March 28, 2026, 7:09 p.m. |
Created at: March 27, 2026, 3:05 p.m.