Triple
T37143458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abbott v. Burke line of cases |
E920176
|
entity |
| Predicate | partyTypeDefendants |
P41330
|
FINISHED |
| Object | New Jersey state education officials |
—
|
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: New Jersey state education officials | Statement: [Abbott v. Burke line of cases, partyTypeDefendants, New Jersey state education officials]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partyTypeDefendants Context triple: [Abbott v. Burke line of cases, partyTypeDefendants, New Jersey state education officials]
-
A.
partyTypePlaintiffs
Indicates the classification or category of the parties serving as plaintiffs in a legal action.
-
B.
coDefendant
Indicates that two or more parties are jointly named and involved as defendants in the same legal case or proceeding.
-
C.
partyToCase
Indicates that an entity is involved in a legal case as one of its formal participants (e.g., plaintiff, defendant, or other party).
-
D.
defendant
Indicates that an entity is the party accused or sued in a legal action or proceeding.
-
E.
typicalDefendants
chosen
Indicates that the referenced entities are the ones most commonly or characteristically serving as defendants in the relevant legal 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_69f76e9e9d008190a250b0387c992c74 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb344c60f8819090f2e21e1e61d621 |
completed | May 6, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69fb2f642db08190b562725502c74ea6 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:15 p.m.