Triple
T386785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Chicago |
E8795
|
entity |
| Predicate | hasNotableFacultyField |
P10780
|
FINISHED |
| Object | economics |
—
|
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: economics | Statement: [University of Chicago, hasNotableFacultyField, economics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFacultyField Context triple: [University of Chicago, hasNotableFacultyField, economics]
-
A.
hasFaculty
Indicates that an institution or department possesses or is associated with one or more faculty members.
-
B.
hasNotableAlumniType
Indicates that an entity has notable alumni belonging to a specified category or type.
-
C.
hasFacultyType
Indicates that a faculty member or academic unit is associated with a specific category or type of faculty (e.g., full-time, adjunct, visiting).
-
D.
hasAcademicStaff
Indicates that an institution or organization employs or is associated with one or more academic staff members.
-
E.
hasAcademicDepartment
Indicates that an institution or organization includes or is associated with a specific academic department.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec447b5481908a5a084787b44ced |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e967d84c8190a6b647f78d95d4e4 |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2ea2dc3088190a2aeb4496aff3582 |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.