Triple
T13303150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jewish Quarter of Girona |
E316864
|
entity |
| Predicate | locatedOnStreetPattern |
P8688
|
FINISHED |
| Object | narrow winding streets |
—
|
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: narrow winding streets | Statement: [Jewish Quarter of Girona, locatedOnStreetPattern, narrow winding streets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedOnStreetPattern Context triple: [Jewish Quarter of Girona, locatedOnStreetPattern, narrow winding streets]
-
A.
streetLocation
Indicates that one entity is located on, along, or at a specific street associated with the other entity.
-
B.
locationPattern
chosen
Indicates a recurring or structured spatial relationship, where an entity consistently appears or is arranged in a particular type of location or spatial configuration.
-
C.
locatedBetweenStreets
Indicates that something is situated between two specified streets, with its position bounded or defined by those streets.
-
D.
hasHeadquartersOnStreet
Indicates that an organization’s headquarters is located on a specific street.
-
E.
hasStreet
Indicates that an entity is located on, associated with, or identified by a particular street.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6893708190aeebf4c47386cff7 |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:28 p.m.