Triple
T12771463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spuistraat |
E305255
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Kalverstraat |
E66237
|
NE 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: Kalverstraat | Statement: [Spuistraat, near, Kalverstraat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalverstraat Context triple: [Spuistraat, near, Kalverstraat]
-
A.
Kalverstraat
chosen
Kalverstraat is one of Amsterdam’s busiest and most famous shopping streets, known for its dense concentration of retail stores and central location.
-
B.
Koningsstraat
Koningsstraat is a major central street in Brussels, Belgium, known for its straight, ceremonial layout and its role connecting key royal and administrative landmarks.
-
C.
Schupstraat
Schupstraat is a prominent street in Antwerp’s Diamond District, known for its dense concentration of diamond shops and related businesses.
-
D.
Servetstraat
Servetstraat is a small historic street located near Dom Square in the center of Utrecht, Netherlands.
-
E.
Roetersstraat
Roetersstraat is a street in Amsterdam that runs through the University of Amsterdam’s Roeterseiland campus area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96df4b36c81909bcc913dd5e535f8 |
completed | April 10, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e25f2c8481908ca9784a8e643a28 |
completed | May 3, 2026, 5:51 a.m. |
Created at: April 9, 2026, 5:28 p.m.