Triple
T21900864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Sherman |
E540803
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Sherman |
—
|
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: Sherman | Statement: [Mike Sherman, familyName, Sherman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sherman Context triple: [Mike Sherman, familyName, Sherman]
-
A.
Sherman
Sherman is a city in north-central Texas that serves as a regional hub for commerce and transportation in the Texoma area.
-
B.
Sherman
Sherman is the bumbling yet kind-hearted scientist protagonist portrayed by Eddie Murphy in the comedy film "The Nutty Professor."
-
C.
Sherman
chosen
Sherman is a surname of English origin borne by numerous notable individuals across politics, military history, and the arts.
-
D.
Sherman
Sherman is the given name of American actor Sherman Hemsley, best known for portraying George Jefferson on the television sitcoms "All in the Family" and "The Jeffersons."
-
E.
Sherman
Sherman is the curious and good-hearted young boy who travels through time with his genius dog guardian in the animated franchise "Mr. Peabody & Sherman."
- 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f11fcb18748190a21071c122b7e6d5 |
completed | April 28, 2026, 8:59 p.m. |
Created at: April 16, 2026, 7:07 p.m.