Triple
T19696938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Steele |
E472984
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Steele |
—
|
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: Steele | Statement: [Michael Steele, familyName, Steele]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steele Context triple: [Michael Steele, familyName, Steele]
-
A.
Steele
chosen
Steele is a surname most notably borne by Charles Steele Jr., an American civil rights leader and former president of the Southern Christian Leadership Conference.
-
B.
Steele
Steele is a historic district of the German city of Essen, known for its riverside location along the Ruhr and its blend of residential, commercial, and cultural areas.
-
C.
Steele
Steele is a small city in southeastern Missouri known for its agricultural surroundings and location in Pemiscot County near the Mississippi River.
-
D.
Steeles
Steeles is a residential and commercial neighbourhood located at the northern edge of Scarborough in Toronto, Ontario, known for its diverse community and suburban character.
-
E.
Blakely
Blakely is a given name and surname of English origin that has become popular as a modern unisex first name.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6421527b08190858788265043792d |
completed | April 20, 2026, 3:11 p.m. |
Created at: April 10, 2026, 1:46 p.m.