Triple
T14818827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tommo |
E348388
|
entity |
| Predicate | commonContext |
P91215
|
FINISHED |
| Object | British English |
—
|
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: British English | Statement: [Tommo, commonContext, British English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonContext Context triple: [Tommo, commonContext, British English]
-
A.
commonMaterialContext
Indicates that two or more entities share a similar or related material composition, substance, or physical makeup.
-
B.
commonApplication
Indicates that multiple entities share or participate in the same application, process, or usage context.
-
C.
canonicalContext
Indicates the standard or primary contextual framework within which an entity, statement, or resource is to be interpreted.
-
D.
contextType
chosen
Indicates the type or category of contextual information associated with an entity or event.
-
E.
commonOn
Indicates that two or more entities share the same location, context, or medium where they are present or occur together.
- 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_69d822eb8f588190bf53445e730a934f |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decfe4cf38819090f25ef045351d5d |
completed | April 14, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69de8c0ef8a4819092d84478b1f56db1 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:50 a.m.