Triple
T34704385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English Marches |
E1000460
|
entity |
| Predicate | resultOfLegalReform |
P99138
|
FINISHED |
| Object | integration into English shire system |
—
|
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: integration into English shire system | Statement: [English Marches, resultOfLegalReform, integration into English shire system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: resultOfLegalReform Context triple: [English Marches, resultOfLegalReform, integration into English shire system]
-
A.
associatedWithLegalReforms
Indicates a relationship where an entity is connected to, involved in, or influenced by specific legal reforms or changes in law.
-
B.
legalStatusAfterReform
Indicates the legal status or condition an entity holds following the implementation of a specific reform or legal change.
-
C.
reformOutcome
chosen
Indicates the result or consequence produced by a particular reform or change initiative.
-
D.
legalReformer
Indicates that an entity works to change, improve, or modernize laws or legal systems.
-
E.
typeOfReforms
Indicates the specific kinds or categories of reforms associated with an entity or situation.
- 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_69f76dab937881909c86f1b9ad50445f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fd32848ea88190a71e6df402bbb30e |
completed | May 8, 2026, 12:47 a.m. |
| PD | Predicate disambiguation | batch_69fd2d7e95588190991d5f21e25155df |
completed | May 8, 2026, 12:25 a.m. |
Created at: May 3, 2026, 3:59 p.m.