Triple
T6788219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stokes |
E155866
|
entity |
| Predicate | usedIn |
P98
|
FINISHED |
| Object | English language |
E211
|
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: English language | Statement: [Stokes, usedIn, English language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: English language Context triple: [Stokes, usedIn, English language]
-
A.
English
chosen
English is a widely spoken West Germanic language that serves as a global lingua franca in education, business, science, and international communication.
-
B.
World English
World English is a phonetic notation system developed by Alexander Melville Bell to represent the sounds of spoken English with precision.
-
C.
Oxford English
Oxford English is a prestigious accent of British English traditionally associated with educated speakers and often used as a standard in broadcasting and formal contexts.
-
D.
Angolalla
Angolalla is a historic town in central Ethiopia known as the birthplace of Emperor Menelik II.
-
E.
English Language Arts
English Language Arts is an academic subject area focused on developing students’ skills in reading, writing, speaking, listening, and language analysis in English.
- 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_69c6881770fc8190972b2906390380f5 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2aa2e0c8190b994261826ae001d |
completed | March 27, 2026, 6:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71a8998408190b741417ce6f21f55 |
completed | March 28, 2026, 12:02 a.m. |
Created at: March 27, 2026, 2:14 p.m.