Triple
T4763050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Local Government (Wales) Act 1994 |
E105741
|
entity |
| Predicate | hasEffectFrom |
P49366
|
FINISHED |
| Object | local government reorganisation date in Wales 1996 |
—
|
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: local government reorganisation date in Wales 1996 | Statement: [Local Government (Wales) Act 1994, hasEffectFrom, local government reorganisation date in Wales 1996]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEffectFrom Context triple: [Local Government (Wales) Act 1994, hasEffectFrom, local government reorganisation date in Wales 1996]
-
A.
hasIntendedEffect
Indicates that one entity is expected or designed to produce a particular effect or outcome on another entity or context.
-
B.
areAffectedBy
Indicates that one entity experiences an effect, influence, or impact as a result of another entity or event.
-
C.
usesEffectType
Indicates that an entity employs or is associated with a particular type or category of effect in its operation or behavior.
-
D.
tookEffect
chosen
Indicates that a change, rule, condition, or event became active, operative, or started producing its intended consequences.
-
E.
hasCommonSideEffect
Indicates that two or more treatments, drugs, or interventions share at least one side effect in common.
- 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_69bd43f14cac819081c7c69803648211 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd686ef1b08190ad60375592c9d6c0 |
completed | March 20, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69bd622807f881908e4bcb14f7731bac |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:20 p.m.