Triple
T27155884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University Park campus |
E682516
|
entity |
| Predicate | hasLawSchoolNearby |
P6776
|
FINISHED |
| Object | Penn State Law |
—
|
NE NERFINISHED |
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: Penn State Law | Statement: [University Park campus, hasLawSchoolNearby, Penn State Law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLawSchoolNearby Context triple: [University Park campus, hasLawSchoolNearby, Penn State Law]
-
A.
lawSchoolName
Indicates the name of the law school with which an entity (such as a person or institution) is associated.
-
B.
hasNearbyInstitution
chosen
Indicates that one entity is located close to or in the immediate vicinity of an institution.
-
C.
hasLegalEducationInstitution
Indicates that an entity is associated with or linked to an institution that provides legal education.
-
D.
associatedSchoolOfLaw
Indicates a relationship where an entity is connected or linked to a particular school of law, typically as its legal education institution or legal academic affiliation.
-
E.
legalSchoolFor
Indicates that one entity is an educational institution recognized or designated as a law school for another entity (such as a person, jurisdiction, or program).
- 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_69eefaceb2a08190b9659b7f730629f5 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f791cc969c8190bf187d6031a030d5 |
completed | May 3, 2026, 6:19 p.m. |
| PD | Predicate disambiguation | batch_69f791033d288190b118029fe412b9c9 |
completed | May 3, 2026, 6:16 p.m. |
Created at: April 27, 2026, 9:16 a.m.