Triple
T29255522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Colorado Law School |
E741692
|
entity |
| Predicate | publicInterestLawReputation |
P193886
|
FINISHED |
| Object | strong |
—
|
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: strong | Statement: [University of Colorado Law School, publicInterestLawReputation, strong]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: publicInterestLawReputation Context triple: [University of Colorado Law School, publicInterestLawReputation, strong]
-
A.
hasLegalRelevanceIn
Indicates that something is legally significant, applicable, or has consequences within a specified legal context, case, or jurisdiction.
-
B.
scopeOfReputation
Indicates the range or extent within which an entity’s reputation is recognized, relevant, or has effect.
-
C.
naturalResourcesLawReputation
Indicates the degree to which an entity is recognized or reputed for expertise, performance, or standing in the field of natural resources law.
-
D.
hasProfessionalReputationFor
Indicates that an entity is recognized by others as being notably associated with a particular professional quality, skill, or behavior.
-
E.
relatedLawDiscoveredBy
Indicates that a particular law is identified as having been discovered or formulated by a specific agent (such as a person or organization).
- 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_69f0911eba2c8190b07cd2fdf91422c9 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69fd592e48cc81909d754cc6c4bd99ae |
completed | May 8, 2026, 3:31 a.m. |
| PD | Predicate disambiguation | batch_69fd58b7f9b881909dc099b28d567784 |
completed | May 8, 2026, 3:30 a.m. |
| PDg | Predicate description generation | batch_69fd592cc56081908ce456114d407616 |
completed | May 8, 2026, 3:31 a.m. |
Created at: April 28, 2026, 12:37 p.m.