Triple
T18988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Göttingen |
E374
|
entity |
| Predicate | hasDoctoralPrograms |
P1453
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [University of Göttingen, hasDoctoralPrograms, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDoctoralPrograms Context triple: [University of Göttingen, hasDoctoralPrograms, yes]
-
A.
hasPublicUniversityCampus
Indicates that a public university maintains or operates a campus at the specified location.
-
B.
hasMajorUniversity
Indicates that a location or region contains at least one prominent, large, or academically significant university.
-
C.
hasMainCampus
Indicates that an educational institution is primarily based at or chiefly associated with a particular campus location.
-
D.
university
Indicates that an educational institution of higher learning is associated with or attended by a given entity.
-
E.
hasFaculty
Indicates that an institution or department possesses or is associated with one or more faculty members.
- 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_69a240778d288190815c0052ebbbcc91 |
completed | Feb. 28, 2026, 1:10 a.m. |
| NER | Named-entity recognition | batch_69a246cbca108190a92478df126d9bf8 |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a2464f61648190ac690044be194972 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a246cb2904819085c13207565a1db2 |
completed | Feb. 28, 2026, 1:37 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.