Triple
T15206211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Almelo |
E363394
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Hagenborgh
Hagenborgh is a notable landmark building in the Dutch city of Almelo, recognized for its prominent role in the local urban landscape.
|
E1150384
|
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: Hagenborgh | Statement: [Almelo, hasLandmark, Hagenborgh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hagenborgh Context triple: [Almelo, hasLandmark, Hagenborgh]
-
A.
Wassenberg
Wassenberg is a historic town in western Germany near the Dutch border, known for its medieval origins and association with the noble House of Wassenberg.
-
B.
Kornhain
Kornhain is a village-level subdivision of the town of Wurzen in the German state of Saxony.
-
C.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
D.
Wiedensahl
Wiedensahl is a small village in Lower Saxony, Germany, best known as the birthplace of the humorist and illustrator Wilhelm Busch.
-
E.
Hakeburg
Hakeburg is a historic castle-like manor and former research facility located in Kleinmachnow, Germany.
- 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: Hagenborgh Triple: [Almelo, hasLandmark, Hagenborgh]
Generated description
Hagenborgh is a notable landmark building in the Dutch city of Almelo, recognized for its prominent role in the local urban landscape.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hagenborgh Target entity description: Hagenborgh is a notable landmark building in the Dutch city of Almelo, recognized for its prominent role in the local urban landscape.
-
A.
Wassenberg
Wassenberg is a historic town in western Germany near the Dutch border, known for its medieval origins and association with the noble House of Wassenberg.
-
B.
Kornhain
Kornhain is a village-level subdivision of the town of Wurzen in the German state of Saxony.
-
C.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
D.
Wiedensahl
Wiedensahl is a small village in Lower Saxony, Germany, best known as the birthplace of the humorist and illustrator Wilhelm Busch.
-
E.
Hakeburg
Hakeburg is a historic castle-like manor and former research facility located in Kleinmachnow, Germany.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006b7964c8190bc8dc3444b94f15e |
completed | April 15, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef88f8ac881908ca32de44b5aa53a |
completed | May 9, 2026, 9:04 a.m. |
| NEDg | Description generation | batch_69fefce7de4881909bed29d98e4c82ce |
completed | May 9, 2026, 9:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fefdaebcf481909bddf548508e32ba |
completed | May 9, 2026, 9:26 a.m. |
Created at: April 10, 2026, 3:11 a.m.