Triple
T2731932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Vowel Shift (early phase) |
E60332
|
entity |
| Predicate | exampleWordAffected |
P14812
|
FINISHED |
| Object | Middle English word "time" |
—
|
LITERAL 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: Middle English word "time" | Statement: [Great Vowel Shift (early phase), exampleWordAffected, Middle English word "time"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleWordAffected Context triple: [Great Vowel Shift (early phase), exampleWordAffected, Middle English word "time"]
-
A.
areAffectedBy
Indicates that one entity experiences an effect, influence, or impact as a result of another entity or event.
-
B.
effectOnSpelling
chosen
Indicates a relationship where one factor influences or alters the way something is spelled.
-
C.
frontAffected
Indicates that an action, event, or condition primarily impacts the front side or front-facing part of an entity.
-
D.
affectsOffice
Indicates that one entity has an influence or impact on the condition, function, or status of an office.
-
E.
affectsProgram
Indicates that one entity produces an influence or change on a program, altering its behavior, state, or outcome.
- F. None of above.
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_69ab4b75cd908190b691ef0d1801acda |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdaf011548190beb9c3feee7b743f |
completed | March 7, 2026, 7:59 a.m. |
| PD | Predicate disambiguation | batch_69abd82859348190bce3be8f2e9d60ba |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.