Triple
T25351678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gajeva Street |
E635702
|
entity |
| Predicate | hasNearbyLandmarks |
P53174
|
FINISHED |
| Object | Ban Jelačić Square |
—
|
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: Ban Jelačić Square | Statement: [Gajeva Street, hasNearbyLandmarks, Ban Jelačić Square]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyLandmarks Context triple: [Gajeva Street, hasNearbyLandmarks, Ban Jelačić Square]
-
A.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
B.
hasFormerNearbyLandmark
Indicates that an entity previously had a nearby landmark that no longer exists or no longer holds the same status or relevance.
-
C.
proximityToLandmark
chosen
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
D.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
E.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
- 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_69e75a9ac5d881909387ed766e20cd47 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f66c5c13808190887180099745673b |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f66abddc448190a488852f8abdeb2c |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 21, 2026, 1:34 p.m.