Triple
T9704242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cecil Sharp |
E234857
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Sharp |
E52476
|
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: Sharp | Statement: [Cecil Sharp, familyName, Sharp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sharp Context triple: [Cecil Sharp, familyName, Sharp]
-
A.
Sharp
chosen
Sharp is a common English surname borne by numerous notable individuals across politics, sports, academia, and the arts.
-
B.
Sharp
Sharp is a Japanese electronics manufacturer best known for producing consumer devices such as mobile phones, televisions, and display technologies.
-
C.
Sharpness
Sharpness is a small port village in Gloucestershire, England, situated on the River Severn and known historically as a key inland dock and terminus for canal traffic.
-
D.
Blunt
Blunt is an English surname borne by various notable figures in the arts, politics, and public life.
-
E.
Quick
Quick is the fast-talking, street-smart protagonist played by Eddie Murphy in the 1989 crime-comedy film "Harlem Nights."
- 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d74afb4819084174aab5bcdb6e0 |
completed | April 1, 2026, 10:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19136b40c8190922052dd84d49f15 |
completed | April 4, 2026, 10:31 p.m. |
Created at: March 30, 2026, 8:18 p.m.