Triple
T2085321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sonia Sotomayor |
E45335
|
entity |
| Predicate | lawReviewRole |
P5610
|
FINISHED |
| Object | Editor of the Yale Law Journal |
—
|
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: Editor of the Yale Law Journal | Statement: [Sonia Sotomayor, lawReviewRole, Editor of the Yale Law Journal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lawReviewRole Context triple: [Sonia Sotomayor, lawReviewRole, Editor of the Yale Law Journal]
-
A.
legalProfessionRole
chosen
Indicates that one entity holds or performs a specific professional role within the legal domain in relation to another entity or context.
-
B.
deJureRole
Indicates that an entity holds a role or position by law or formal right, regardless of whether it is exercised in practice.
-
C.
legalCaseRole
Indicates the specific role or capacity an entity holds within a legal case, such as plaintiff, defendant, judge, or attorney.
-
D.
courtRole
Indicates the specific capacity or position an entity holds within a court proceeding or judicial context.
-
E.
lawReview
Indicates a relationship where an entity is associated with a law review, typically as its subject, source, or venue of publication within legal scholarship.
- 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_69a8891869c88190a02643e3bb746f59 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba53d4488190a7d9eabcb6904e8e |
completed | March 7, 2026, 5:40 a.m. |
| PD | Predicate disambiguation | batch_69abb7b4356881909217c42ccb8bb1ed |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:41 p.m.