Triple
T17918470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter G. Schultz |
E447999
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Peter G. Schultz |
—
|
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: Peter G. Schultz | Statement: [Peter G. Schultz, name, Peter G. Schultz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter G. Schultz Context triple: [Peter G. Schultz, name, Peter G. Schultz]
-
A.
Peter G. Schultz
chosen
Peter G. Schultz is an American chemist renowned for pioneering work in chemical biology, including the expansion of the genetic code and the development of novel protein engineering technologies.
-
B.
Peter T. Grauer
Peter T. Grauer is an American business executive best known as the longtime chairman of Bloomberg L.P.
-
C.
Erik O. Schulz
Erik O. Schulz is a German politician who serves as the mayor of the city of Hagen in North Rhine-Westphalia.
-
D.
Michael T. Sauer
Michael T. Sauer was an American judge best known for presiding over high-profile criminal cases in Los Angeles County.
-
E.
Charles J.D. Schlissel
Charles J.D. Schlissel is a film producer best known for his work on major Hollywood thrillers such as "Flightplan."
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a30844548190b7a43c2f093f35d7 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 10:20 a.m.