Triple
T37823065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monza urban area |
E942976
|
entity |
| Predicate | partOfLargerUrbanRegion |
P294
|
FINISHED |
| Object | Milan metropolitan area |
—
|
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: Milan metropolitan area | Statement: [Monza urban area, partOfLargerUrbanRegion, Milan metropolitan area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfLargerUrbanRegion Context triple: [Monza urban area, partOfLargerUrbanRegion, Milan metropolitan area]
-
A.
partOfMetropolitanArea
chosen
Indicates that one place is included within and belongs to the larger metropolitan area of another place.
-
B.
nearestLargeUrbanArea
Indicates that one entity is the closest major city or large urban center to the other entity.
-
C.
relatedUrbanArea
Indicates that one urban area is geographically or functionally associated with another urban area, such as being nearby, connected, or part of the same broader metropolitan context.
-
D.
containsSuburbanAreaOf
Indicates that one geographic region includes within its boundaries a suburban area belonging to or associated with another region.
-
E.
significantUrbanArea
Indicates that a location is classified as a major or important urban center within a broader geographic or administrative context.
- 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_69f76ee987588190906506e759be5db3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a01926e2f348190a632eff5c91db5e0 |
completed | May 11, 2026, 8:25 a.m. |
| PD | Predicate disambiguation | batch_6a01923488f4819094d79a27f4bc8ab8 |
completed | May 11, 2026, 8:24 a.m. |
Created at: May 3, 2026, 4:19 p.m.