Triple
T2707231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oppenheimer–Volkoff limit |
E59370
|
entity |
| Predicate | derivedBy |
P29592
|
FINISHED |
| Object | George Volkoff |
E290119
|
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 Volkoff | Statement: [Oppenheimer–Volkoff limit, derivedBy, George Volkoff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Volkoff Context triple: [Oppenheimer–Volkoff limit, derivedBy, George Volkoff]
-
A.
George Volkoff
chosen
George Volkoff was a Canadian theoretical physicist known for his pioneering work on the structure of neutron stars, which led to the formulation of the Oppenheimer–Volkoff limit.
-
B.
Clarence Kolster
Clarence Kolster was an American film editor best known for his work on classic Hollywood films in the early 20th century.
-
C.
Mike Todd
Mike Todd was an American film producer best known for producing the Oscar-winning epic "Around the World in 80 Days" and for being the third husband of actress Elizabeth Taylor.
-
D.
Don Brochu
Don Brochu is a film editor best known for his work on major Hollywood movies, including the hit thriller "The Bodyguard."
-
E.
Allen Bauer
Allen Bauer is the romantic lead in the 1984 fantasy-comedy film "Splash," where he falls in love with a mysterious mermaid in New York City.
- 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_69ab4ac66bc88190b9e4afa5fc843f72 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda725f24819090e8d936b3d2d5bc |
completed | March 7, 2026, 7:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afb683092c8190859e92acadfb820c |
completed | March 10, 2026, 6:13 a.m. |
Created at: March 6, 2026, 9:55 p.m.