Triple
T1729268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zelman v. Simmons-Harris |
E37571
|
entity |
| Predicate | programCharacteristics |
P29048
|
FINISHED |
| Object | program was neutral with respect to religion |
—
|
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: program was neutral with respect to religion | Statement: [Zelman v. Simmons-Harris, programCharacteristics, program was neutral with respect to religion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: programCharacteristics Context triple: [Zelman v. Simmons-Harris, programCharacteristics, program was neutral with respect to religion]
-
A.
programType
Indicates the category or kind of program to which an entity belongs or with which it is associated.
-
B.
courseSetupCharacteristic
Indicates a defining setup-related property or configuration aspect associated with a course.
-
C.
serviceCharacterization
chosen
Indicates how a service is defined, described, or classified in terms of its properties, behavior, or role.
-
D.
equipmentCharacteristic
Indicates that a specific characteristic, property, or attribute is associated with a piece of equipment.
-
E.
policyCharacteristic
Indicates that a policy possesses a particular attribute, feature, or quality that characterizes how it is defined or operates.
- 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_69a8861acab88190bb43cde203429399 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab5c553e508190b0f511b05e07fa20 |
completed | March 6, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69aa61c25a648190892de94c997fb983 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.