Triple
T411700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National University of Colombia (Bogotá campus) |
E9502
|
entity |
| Predicate | foundedAsCampus |
P11180
|
FINISHED |
| Object | 1930s |
—
|
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: 1930s | Statement: [National University of Colombia (Bogotá campus), foundedAsCampus, 1930s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: foundedAsCampus Context triple: [National University of Colombia (Bogotá campus), foundedAsCampus, 1930s]
-
A.
hasMainCampus
Indicates that an educational institution is primarily based at or chiefly associated with a particular campus location.
-
B.
foundedAs
Indicates the original name or form under which an organization, institution, or entity was first established.
-
C.
cityCampus
Indicates that a campus is located within or associated with a particular city.
-
D.
hasAdditionalCampus
Indicates that an educational institution maintains one or more campuses in addition to its primary or main campus.
-
E.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
- 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ecdafa2481908111accc918ff2e8 |
completed | Feb. 28, 2026, 1:25 p.m. |
| PD | Predicate disambiguation | batch_69a2e9749234819084b0ce94faabd0b1 |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2ea60e590819081779a6510918d9b |
completed | Feb. 28, 2026, 1:15 p.m. |
Created at: Feb. 28, 2026, 1:09 p.m.