Triple
T19720828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Groningen |
E473602
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Vismarkt
Vismarkt is a historic central market square in the Dutch city of Groningen, known for its traditional stalls, surrounding monumental buildings, and vibrant urban life.
|
E1391797
|
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: Vismarkt | Statement: [Groningen, hasLandmark, Vismarkt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vismarkt Context triple: [Groningen, hasLandmark, Vismarkt]
-
A.
Markec
Markec is a South Slavic diminutive form of the male given name Marko, used as an affectionate or familiar nickname.
-
B.
Marktl
Marktl is a small Bavarian municipality best known as the birthplace of Pope Benedict XVI.
-
C.
Marktsteft
Marktsteft is a small town in the Lower Franconia region of Bavaria, Germany, situated on the Main River and known for its historic harbor and wine-growing surroundings.
-
D.
Markt
Markt is the central market square of Bruges, Belgium, known for its historic guild houses, bustling cafes, and prominent Belfry tower.
-
E.
Marktbreit
Marktbreit is a small historic town in Bavaria, Germany, best known as the birthplace of psychiatrist and neuropathologist Alois Alzheimer.
- 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: Vismarkt Triple: [Groningen, hasLandmark, Vismarkt]
Generated description
Vismarkt is a historic central market square in the Dutch city of Groningen, known for its traditional stalls, surrounding monumental buildings, and vibrant urban life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vismarkt Target entity description: Vismarkt is a historic central market square in the Dutch city of Groningen, known for its traditional stalls, surrounding monumental buildings, and vibrant urban life.
-
A.
Markec
Markec is a South Slavic diminutive form of the male given name Marko, used as an affectionate or familiar nickname.
-
B.
Marktl
Marktl is a small Bavarian municipality best known as the birthplace of Pope Benedict XVI.
-
C.
Marktsteft
Marktsteft is a small town in the Lower Franconia region of Bavaria, Germany, situated on the Main River and known for its historic harbor and wine-growing surroundings.
-
D.
Markt
Markt is the central market square of Bruges, Belgium, known for its historic guild houses, bustling cafes, and prominent Belfry tower.
-
E.
Marktbreit
Marktbreit is a small historic town in Bavaria, Germany, best known as the birthplace of psychiatrist and neuropathologist Alois Alzheimer.
- 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_69d8e516dd048190a0b6c93ea3e71f58 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e649f483c481908c6b3114bf9c5934 |
completed | April 20, 2026, 3:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07aba719b48190a8ce14facba8f7d6 |
completed | May 15, 2026, 11:26 p.m. |
| NEDg | Description generation | batch_6a07adc08ca081908df09c099cb710c2 |
completed | May 15, 2026, 11:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07ae4ba5a88190a6ab297fb99cdd9d |
completed | May 15, 2026, 11:37 p.m. |
Created at: April 10, 2026, 1:46 p.m.