Triple
T3756313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gebhart v. Belton |
E82055
|
entity |
| Predicate | decisionDirection |
P50766
|
FINISHED |
| Object | in favor of Black student plaintiffs |
—
|
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: in favor of Black student plaintiffs | Statement: [Gebhart v. Belton, decisionDirection, in favor of Black student plaintiffs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: decisionDirection Context triple: [Gebhart v. Belton, decisionDirection, in favor of Black student plaintiffs]
-
A.
decisionType
Indicates the specific category or nature of a decision associated with an entity or event.
-
B.
decisionStyle
Indicates the characteristic way an entity approaches, processes, and makes decisions in a given context.
-
C.
decisionLanguage
Indicates that a decision, statement, or choice is expressed or recorded in a particular natural language.
-
D.
decisionMaking
Indicates the process by which an entity evaluates options and selects a course of action among alternatives.
-
E.
decisionEffect
Indicates that one decision leads to, influences, or determines a particular outcome or consequence.
- F. None of above. chosen
Provenance (4 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbbe7a6081909b0f835a77941300 |
completed | March 8, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69adc04c851c8190ae5eaebf36df539b |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc0fe3e3c8190bd886c7745c172a0 |
completed | March 8, 2026, 6:33 p.m. |
Created at: March 8, 2026, 3:35 p.m.