Triple
T14552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of California, Berkeley |
E290
|
entity |
| Predicate | students |
P48
|
FINISHED |
| Object | over 40,000 |
—
|
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: over 40,000 | Statement: [University of California, Berkeley, students, over 40,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: students Context triple: [University of California, Berkeley, students, over 40,000]
-
A.
hasStudents
chosen
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
B.
educationSystem
Indicates the relationship in which an entity is part of, governed by, or operates within a particular system or structure of education.
-
C.
campus
Indicates that an entity is located on, associated with, or taking place within a particular campus.
-
D.
educates
Indicates that one entity provides instruction, knowledge, or training to another entity.
-
E.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a240b249788190af8dbf7e80e9c91b |
completed | Feb. 28, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69a23feae8c481908d8c50faac01fc5c |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.