Triple
T25687249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chapter XXII – Criminal intimidation, insult and annoyance |
E644095
|
entity |
| Predicate | hasPenaltyType |
P166927
|
FINISHED |
| Object | imprisonment |
—
|
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: imprisonment | Statement: [Chapter XXII – Criminal intimidation, insult and annoyance, hasPenaltyType, imprisonment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPenaltyType Context triple: [Chapter XXII – Criminal intimidation, insult and annoyance, hasPenaltyType, imprisonment]
-
A.
penaltyAppliesTo
Indicates that a specific penalty is imposed on, or is relevant to, a particular entity or situation.
-
B.
penaltyTypes
chosen
Indicates the kinds or categories of penalties that are associated with or applied to an entity or action.
-
C.
penaltyStatus
Indicates the current state or condition of a penalty that has been assigned or is applicable in a given context.
-
D.
hasPunishment
Indicates that an entity is subject to a specified penalty, sanction, or adverse consequence as a result of some action, condition, or rule.
-
E.
hasCanonicalPenalty
Indicates that an entity is subject to an officially established or authoritative penalty defined by a governing canon or rule system.
- 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_69e77e8046888190b07ffa58c7e2c37a |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f67c9fe7b48190b79b4041357edb49 |
completed | May 2, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69f678cc272081909e5c70f1bc7407f0 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 21, 2026, 8:10 p.m.