Triple
T6300140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Livesey |
E141231
|
entity |
| Predicate | legalRoleInStory |
P41321
|
FINISHED |
| Object | local magistrate |
—
|
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 magistrate | Statement: [Dr. Livesey, legalRoleInStory, local magistrate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalRoleInStory Context triple: [Dr. Livesey, legalRoleInStory, local magistrate]
-
A.
legalCaseRole
Indicates the specific role or capacity an entity holds within a legal case, such as plaintiff, defendant, judge, or attorney.
-
B.
roleInStories
Indicates the specific function, position, or character part an entity plays within one or more stories.
-
C.
legalSystemRole
chosen
Indicates the specific function, capacity, or position an entity holds within a legal or judicial system.
-
D.
roleInText
Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
-
E.
legalProfessionRole
Indicates that one entity holds or performs a specific professional role within the legal domain in relation to another entity or context.
- 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_69c008cf0ad4819095def81e2bd42f9f |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0645a7e048190af7a609fc74b876a |
completed | March 22, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69c060e311b48190b1c74a5cf9435623 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:27 p.m.