Triple
T1894746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Lieutenant of Berkshire |
E41952
|
entity |
| Predicate | scopeOfActivities |
P1164
|
FINISHED |
| Object | civic life in Berkshire |
—
|
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: civic life in Berkshire | Statement: [Lord Lieutenant of Berkshire, scopeOfActivities, civic life in Berkshire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scopeOfActivities Context triple: [Lord Lieutenant of Berkshire, scopeOfActivities, civic life in Berkshire]
-
A.
scopeOfContribution
Indicates the specific area, domain, or extent within which an entity’s contribution or involvement applies.
-
B.
scopeOfProducts
Indicates the range or extent of products that fall under a particular category, responsibility, or context.
-
C.
scopeOfUse
Indicates the range, context, or conditions under which something is intended, allowed, or applicable to be used.
-
D.
typicalActivity
chosen
Indicates that an entity is commonly or characteristically engaged in a particular activity.
-
E.
operationalScope
Indicates the range, extent, or boundaries within which an entity, process, or activity is authorized or designed to operate.
- 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb16c09e48190a345c95eab59fd87 |
completed | March 7, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69abafe7e7e88190b58c0df59187c0c2 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:34 p.m.