Triple
T12108347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peyton Place |
E288359
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | George Boemler |
E52466
|
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: George Boemler | Statement: [Peyton Place, editor, George Boemler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Boemler Context triple: [Peyton Place, editor, George Boemler]
-
A.
George Boemler
chosen
George Boemler was a film editor known for his work on classic Hollywood productions, including the musical comedy "High Society."
-
B.
Charles Bohl
Charles Bohl is a screenwriter best known for his work on the 2002 psychological thriller film "Swimfan."
-
C.
George Weisgerber
George Weisgerber is an American reality television personality best known for appearing as a contestant on the VH1 dating show "I Love New York 2."
-
D.
George Ratterman
George Ratterman was an American professional football quarterback who later became a prominent sports broadcaster and attorney.
-
E.
Arthur Hohl
Arthur Hohl was an American character actor known for his supporting roles in numerous Hollywood films during the 1930s and 1940s.
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915632dc48190863e0239cef37e24 |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c1d31048190ab0a8d8e00515211 |
completed | May 8, 2026, 2:36 a.m. |
Created at: April 8, 2026, 9:49 p.m.