Triple
T5064030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bredevoort |
E114099
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Boekenstad
Boekenstad is the Dutch nickname for the small town of Bredevoort, renowned for its many bookshops and literary events.
|
E489723
|
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: Boekenstad | Statement: [Bredevoort, nickname, Boekenstad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Boekenstad Context triple: [Bredevoort, nickname, Boekenstad]
-
A.
Lichtstad
Lichtstad is the Dutch nickname for the city of Eindhoven, reflecting its historic association with the lighting industry and companies like Philips.
-
B.
Mauritsstad
Mauritsstad was the 17th-century Dutch colonial capital in northeastern Brazil, known for its planned urban layout and role as an administrative and commercial center under Dutch rule.
-
C.
Binnenstad
Binnenstad is the historic city center of Utrecht in the Netherlands, known for its medieval architecture, canals, and cultural landmarks.
-
D.
City of Holland
The City of Holland is a Michigan community known for its Dutch heritage, tulip festivals, and attractions like historic windmills and themed gardens.
-
E.
Stad
Stad is a coastal municipality and peninsula in Vestland county, Norway, known for its exposed headland and hazardous maritime waters.
- 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: Boekenstad Triple: [Bredevoort, nickname, Boekenstad]
Generated description
Boekenstad is the Dutch nickname for the small town of Bredevoort, renowned for its many bookshops and literary events.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Boekenstad Target entity description: Boekenstad is the Dutch nickname for the small town of Bredevoort, renowned for its many bookshops and literary events.
-
A.
Lichtstad
Lichtstad is the Dutch nickname for the city of Eindhoven, reflecting its historic association with the lighting industry and companies like Philips.
-
B.
Mauritsstad
Mauritsstad was the 17th-century Dutch colonial capital in northeastern Brazil, known for its planned urban layout and role as an administrative and commercial center under Dutch rule.
-
C.
Binnenstad
Binnenstad is the historic city center of Utrecht in the Netherlands, known for its medieval architecture, canals, and cultural landmarks.
-
D.
City of Holland
The City of Holland is a Michigan community known for its Dutch heritage, tulip festivals, and attractions like historic windmills and themed gardens.
-
E.
Stad
Stad is a coastal municipality and peninsula in Vestland county, Norway, known for its exposed headland and hazardous maritime waters.
- 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_69bd443c0c8c81908663b77afb28e165 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd747756bc8190863c426e6fd6e8f7 |
completed | March 20, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bea49ae8c081908ef8c2e2dbbe3b49 |
completed | March 21, 2026, 2 p.m. |
| NEDg | Description generation | batch_69bea56407148190a2ba646c779e738b |
completed | March 21, 2026, 2:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bea610b7ac8190b0dcfaa67ba80431 |
completed | March 21, 2026, 2:07 p.m. |
Created at: March 20, 2026, 1:38 p.m.