Triple
T4054819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Human Rights Act 1998 |
E84667
|
entity |
| Predicate | section10Effect |
P53038
|
FINISHED |
| Object | provides for remedial orders to amend incompatible legislation |
—
|
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: provides for remedial orders to amend incompatible legislation | Statement: [Human Rights Act 1998, section10Effect, provides for remedial orders to amend incompatible legislation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: section10Effect Context triple: [Human Rights Act 1998, section10Effect, provides for remedial orders to amend incompatible legislation]
-
A.
sideEffect
Indicates that one entity is an unintended or secondary effect resulting from the use or occurrence of another entity.
-
B.
notableEffect
Indicates that one entity has a significant impact, consequence, or influence on another entity or situation.
-
C.
primaryEffect
Indicates the main direct outcome or consequence that results from a given cause, action, or condition.
-
D.
placeOfEffect
Indicates the location or setting where an action, event, or effect takes place or is realized.
-
E.
effectOnSchedule
Indicates how an event, action, or condition changes, disrupts, or influences a planned schedule or timeline.
- F. None of above. chosen
Provenance (4 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_69aed933bec881909edfa28ebb69c634 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefbaa84b88190856e179266769e6d |
completed | March 9, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69aef90249e4819095e9e043bc4aa9a6 |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aef9fcb31c819098d5287b6fc84f4e |
completed | March 9, 2026, 4:49 p.m. |
Created at: March 9, 2026, 3:38 p.m.