Triple
T187198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yale College residential colleges |
E4006
|
entity |
| Predicate | numberOfColleges |
P6738
|
FINISHED |
| Object | 14 |
—
|
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: 14 | Statement: [Yale College residential colleges, numberOfColleges, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfColleges Context triple: [Yale College residential colleges, numberOfColleges, 14]
-
A.
college
Indicates that an entity is a college-level educational institution attended by or associated with another entity.
-
B.
includesPublicUniversities
Indicates that the subject set or collection contains one or more public universities as members.
-
C.
university
Indicates that an educational institution of higher learning is associated with or attended by a given entity.
-
D.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
-
E.
hasMajorUniversity
Indicates that a location or region contains at least one prominent, large, or academically significant university.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a2594940e48190a3d8efbce46241c3 |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a25670feb081908e26a2543ebe7b3a |
completed | Feb. 28, 2026, 2:44 a.m. |
| PDg | Predicate description generation | batch_69a257e763d081908c54ad57d8d3060d |
completed | Feb. 28, 2026, 2:50 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.