Triple
T10548804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Sherman |
E248890
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Sherman |
E558644
|
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: Sherman | Statement: [John Sherman, familyName, Sherman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sherman Context triple: [John 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.
The General
The General is a famous 19th-century American steam locomotive best known for its central role in the Civil War’s Great Locomotive Chase of 1862.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d526d305d081909b48d244e1cfa092 |
completed | April 7, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d93457af7c819090f576ae606c5849 |
completed | April 10, 2026, 5:33 p.m. |
Created at: April 6, 2026, 12:33 p.m.