Triple
T15231861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deputy Lieutenant |
E364021
|
entity |
| Predicate | officeHoldersNumberLimitedBy |
P47665
|
FINISHED |
| Object | population of lieutenancy area |
—
|
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: population of lieutenancy area | Statement: [Deputy Lieutenant, officeHoldersNumberLimitedBy, population of lieutenancy area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeHoldersNumberLimitedBy Context triple: [Deputy Lieutenant, officeHoldersNumberLimitedBy, population of lieutenancy area]
-
A.
officeHoldersNumberLimit
chosen
Indicates a constraint specifying the maximum number of individuals who may simultaneously hold a particular office or position.
-
B.
officeHoldersLimitedBy
Indicates that the number or scope of office holders for a given position or role is restricted or capped by a specified limit or condition.
-
C.
officeHoldersNumber
Indicates the number of individuals who hold a particular office or position.
-
D.
officeHolderCountIncludes
Indicates that a specified count or total explicitly includes the number of individuals holding a particular office or position.
-
E.
firstOfficeHoldersCount
Indicates the number of individuals who initially held a particular office or position.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078e27408190bc13c0ca441f5594 |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca899d5c8190be4a7c71e1683c69 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:12 a.m.