Triple
T19920603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loeb |
E478783
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Carl M. Loeb |
—
|
NE NERFINISHED |
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: Carl M. Loeb | Statement: [Loeb, hasNotableBearer, Carl M. Loeb]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carl M. Loeb Context triple: [Loeb, hasNotableBearer, Carl M. Loeb]
-
A.
Carl M. Loeb
chosen
Carl M. Loeb was a prominent American investment banker and financier, best known as a co-founder of the Wall Street firm Loeb, Rhoades & Co.
-
B.
Robert H. Loeb
Robert H. Loeb was an American physician and influential clinical researcher known for his contributions to internal medicine and medical education.
-
C.
Milton J. Rosenberg
Milton J. Rosenberg was an American social psychologist and long-time Chicago radio talk show host known for his influential program "Extension 720."
-
D.
Robert B. Hauser
Robert B. Hauser is a cinematographer best known for his work on the film "Gideon's Trumpet."
-
E.
Arthur A. Ross
Arthur A. Ross was an American screenwriter known for his work on mid-20th-century Hollywood films and genre pictures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659c564788190a3893fc73fc4922b |
completed | April 20, 2026, 4:52 p.m. |
Created at: April 10, 2026, 1:53 p.m.