Triple
T15319649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niguarda |
E366253
|
entity |
| Predicate | hasNearbyGreenCorridor |
P33602
|
FINISHED |
| Object | urban green belt of northern Milan |
—
|
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: urban green belt of northern Milan | Statement: [Niguarda, hasNearbyGreenCorridor, urban green belt of northern Milan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyGreenCorridor Context triple: [Niguarda, hasNearbyGreenCorridor, urban green belt of northern Milan]
-
A.
hasNearbyTransportationCorridor
Indicates that an entity is located close to a significant transportation route or corridor, such as a road, railway, or transit line.
-
B.
hasAdjacentStationOnGreenLine
Indicates that one station is directly next to another station along the Green Line.
-
C.
hasNearbyGate
Indicates that one entity has a gate located in close physical proximity to it.
-
D.
hasNearbyGreenSpace
chosen
Indicates that an entity is located close to an area of green space, such as a park, garden, or natural vegetation.
-
E.
hasNearbyCrossingPoint
Indicates that one location has a crossing point (such as a bridge, crosswalk, or intersection) situated close 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd356b881908f054b64eee6a371 |
completed | April 16, 2026, 1:39 a.m. |
| PD | Predicate disambiguation | batch_69deca9659f48190b8661df223ce5078 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:16 a.m.