Triple
T12681693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penn Wharton Public Policy Initiative |
E302961
|
entity |
| Predicate | usesExpertiseFrom |
P33840
|
FINISHED |
| Object | Wharton faculty |
—
|
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: Wharton faculty | Statement: [Penn Wharton Public Policy Initiative, usesExpertiseFrom, Wharton faculty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesExpertiseFrom Context triple: [Penn Wharton Public Policy Initiative, usesExpertiseFrom, Wharton faculty]
-
A.
usesKnowledgeOf
chosen
Indicates that one entity applies or draws upon the knowledge possessed by another entity in performing an action or achieving a result.
-
B.
skilledIn
Indicates that an entity possesses ability, expertise, or proficiency in performing or using another entity (such as a task, tool, or domain).
-
C.
hasDesignExpertise
Indicates that one entity possesses specialized knowledge or skill in design related to another entity.
-
D.
basedOnExperience
Indicates that something is determined, chosen, or formed according to prior experience or experiential knowledge.
-
E.
indicatesSkill
Indicates a relationship where one entity possesses, demonstrates, or is associated with a particular skill represented by another entity.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961b32dbc81908101fc5f07e26ed3 |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:21 p.m.