Triple
T3959014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Normans |
E85853
|
entity |
| Predicate | legalInfluenceOn |
P24435
|
FINISHED |
| Object | English law |
—
|
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: English law | Statement: [Normans, legalInfluenceOn, English law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalInfluenceOn Context triple: [Normans, legalInfluenceOn, English law]
-
A.
courtInfluence
Indicates that one party exerts or holds influence over decisions, outcomes, or behavior within a court or judicial setting.
-
B.
legalDoctrineInfluenced
Indicates that one legal doctrine has shaped, informed, or contributed to the development or interpretation of another legal doctrine.
-
C.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
-
D.
influencedCourtDecision
Indicates that one entity had an effect on or contributed to the outcome of a court’s decision regarding another entity or matter.
-
E.
legalTraditionInfluenced
chosen
Indicates that one legal tradition has had a formative or shaping influence on the development, principles, or practices of another legal tradition.
- 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_69aed93a96908190bcbdbfa718f155bd |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefba878a48190a2e234d775215938 |
completed | March 9, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69aef8efcf3c81908ccf61d9ce26b0c0 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:31 p.m.