Triple
T15863986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Groot Begijnhof Leuven |
E384661
|
entity |
| Predicate | numberOfStreetsAndAlleys |
P96133
|
FINISHED |
| Object | about 12 |
—
|
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: about 12 | Statement: [Groot Begijnhof Leuven, numberOfStreetsAndAlleys, about 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStreetsAndAlleys Context triple: [Groot Begijnhof Leuven, numberOfStreetsAndAlleys, about 12]
-
A.
hasNumberOfStreets
chosen
Indicates the relationship that specifies how many streets are associated with or contained within a given entity.
-
B.
numberOfConvergingStreets
Indicates the count of distinct streets that meet or intersect at a particular junction or location.
-
C.
numberOfRoadways
Indicates the count of distinct roadways associated with or present at a given entity or location.
-
D.
streetSet
Indicates that a particular street belongs to, or is included within, a specified set or collection of streets.
-
E.
betweenStreets
Indicates that one location is situated between two specified streets, typically along a road segment bounded by those streets.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142b976c081908d3ba3e705419f3a |
completed | April 16, 2026, 8:12 p.m. |
Created at: April 10, 2026, 4:50 a.m.