Triple
T9020663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markt (Bruges) |
E215712
|
entity |
| Predicate | hasNearbyStreet |
P8235
|
FINISHED |
| Object |
Vlamingstraat
Vlamingstraat is a central street in Bruges, Belgium, known for its historic buildings, shops, and proximity to the city’s main square, the Markt.
|
E791144
|
NE FINISHED |
How this triple was built (4 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: Vlamingstraat | Statement: [Markt (Bruges), hasNearbyStreet, Vlamingstraat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vlamingstraat Context triple: [Markt (Bruges), hasNearbyStreet, Vlamingstraat]
-
A.
Valckenierstraat
Valckenierstraat is a street in Amsterdam, Netherlands, located near the University of Amsterdam’s Roeterseiland Campus.
-
B.
Hoveniersstraat
Hoveniersstraat is a prominent street in Antwerp, Belgium, renowned as a central hub of the city's diamond trade and industry.
-
C.
Wibautstraat
Wibautstraat is a metro station in Amsterdam that serves as a stop on the city’s rapid transit network.
-
D.
Kalverstraat
Kalverstraat is one of Amsterdam’s busiest and most famous shopping streets, known for its dense concentration of retail stores and central location.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Vlamingstraat Triple: [Markt (Bruges), hasNearbyStreet, Vlamingstraat]
Generated description
Vlamingstraat is a central street in Bruges, Belgium, known for its historic buildings, shops, and proximity to the city’s main square, the Markt.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vlamingstraat Target entity description: Vlamingstraat is a central street in Bruges, Belgium, known for its historic buildings, shops, and proximity to the city’s main square, the Markt.
-
A.
Valckenierstraat
Valckenierstraat is a street in Amsterdam, Netherlands, located near the University of Amsterdam’s Roeterseiland Campus.
-
B.
Hoveniersstraat
Hoveniersstraat is a prominent street in Antwerp, Belgium, renowned as a central hub of the city's diamond trade and industry.
-
C.
Wibautstraat
Wibautstraat is a metro station in Amsterdam that serves as a stop on the city’s rapid transit network.
-
D.
Kalverstraat
Kalverstraat is one of Amsterdam’s busiest and most famous shopping streets, known for its dense concentration of retail stores and central location.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69ca83a38aa88190bf1bb80c4548b5e2 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6a421c2c8190abb12c826066fe75 |
completed | April 1, 2026, 12:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0c6d4a5d08190832f41dd9dc82f41 |
completed | April 4, 2026, 8:07 a.m. |
| NEDg | Description generation | batch_69d0c990fefc81908b570d4caa79378f |
completed | April 4, 2026, 8:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0cb34433c81909b331c6719e27734 |
completed | April 4, 2026, 8:26 a.m. |
Created at: March 30, 2026, 7:07 p.m.