Triple
T24952761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parkville campus |
E624383
|
entity |
| Predicate | nearestCentralBusinessDistrict |
P34630
|
FINISHED |
| Object | Melbourne central business district |
—
|
NE NERFINISHED |
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: Melbourne central business district | Statement: [Parkville campus, nearestCentralBusinessDistrict, Melbourne central business district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearestCentralBusinessDistrict Context triple: [Parkville campus, nearestCentralBusinessDistrict, Melbourne central business district]
-
A.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
B.
nearDowntown
chosen
Indicates that one location is situated close to or within a short distance of a city’s downtown area.
-
C.
nearMetroStation
Indicates that one entity is located close to or within a short walking distance of a metro (subway) station.
-
D.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
E.
nearestMajorMetro
Indicates the relationship where a given location is associated with the closest large metropolitan area to it.
- 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_69e2ff22e4c48190a0444b5a044f14e8 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d77f6e88190a4643ab2cbef567b |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 5:57 a.m.