Triple
T28361969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lynch v. Donnelly |
E718384
|
entity |
| Predicate | hasDefendantOrRespondent |
P43041
|
FINISHED |
| Object | Daniel Donnelly |
—
|
NE NERFINISHED |
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: Daniel Donnelly | Statement: [Lynch v. Donnelly, hasDefendantOrRespondent, Daniel Donnelly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDefendantOrRespondent Context triple: [Lynch v. Donnelly, hasDefendantOrRespondent, Daniel Donnelly]
-
A.
hasDefendants
chosen
Indicates that one or more entities serve as defendants in relation to a particular legal case or proceeding.
-
B.
hasMainDefendant
Indicates that a legal case or proceeding identifies a specific individual or entity as its primary defendant.
-
C.
hasDefendantWork
Indicates that a particular work (such as a product, creation, or item) is associated with or attributed to the defendant in a legal context.
-
D.
hasDefendantService
Indicates that a particular method, action, or process is used to formally serve legal notice or documents to a defendant in a legal proceeding.
-
E.
defendantStatus
Indicates the legal condition or standing of a defendant within a judicial or law-enforcement process.
- 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_69eff6ed5af48190be4e0adf298223e0 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69fb2e940d5c8190bceae77daf4ef512 |
completed | May 6, 2026, 12:05 p.m. |
| PD | Predicate disambiguation | batch_69f9fec70bd881909c658a3c5020318b |
completed | May 5, 2026, 2:29 p.m. |
Created at: April 28, 2026, 12:52 a.m.