Triple
T411733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National University of Colombia (Bogotá campus) |
E9502
|
entity |
| Predicate | hasGreenArea |
P5383
|
FINISHED |
| Object | large central campus park |
—
|
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: large central campus park | Statement: [National University of Colombia (Bogotá campus), hasGreenArea, large central campus park]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGreenArea Context triple: [National University of Colombia (Bogotá campus), hasGreenArea, large central campus park]
-
A.
hasRecreationalArea
chosen
Indicates that an entity includes, provides, or is associated with a designated space intended for leisure or recreational activities.
-
B.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
C.
hasResidentialArea
Indicates that an entity includes, contains, or is associated with an area designated for people to live or reside.
-
D.
hasIrrigationArea
Indicates that an entity possesses or is associated with a specific area of land equipped or designated for irrigation.
-
E.
hasTrees
Indicates that something possesses or contains one or more trees.
- 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_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. |
Created at: Feb. 28, 2026, 1:09 p.m.