Triple
T11744013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Starogard Gdański |
E279225
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Diepholz
Diepholz is a town in Lower Saxony, Germany, known as a local administrative center and for its surrounding lake district and agricultural landscape.
|
E993753
|
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: Diepholz | Statement: [Starogard Gdański, hasTwinTown, Diepholz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diepholz Context triple: [Starogard Gdański, hasTwinTown, Diepholz]
-
A.
Datteln
Datteln is a town in North Rhine-Westphalia, Germany, known for its canal junction and industrial heritage.
-
B.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
C.
Dorsten
Dorsten is a town in North Rhine-Westphalia, Germany, located in the Ruhr area and known for its mix of industrial heritage and nearby natural landscapes.
-
D.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
E.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
- 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: Diepholz Triple: [Starogard Gdański, hasTwinTown, Diepholz]
Generated description
Diepholz is a town in Lower Saxony, Germany, known as a local administrative center and for its surrounding lake district and agricultural landscape.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Diepholz Target entity description: Diepholz is a town in Lower Saxony, Germany, known as a local administrative center and for its surrounding lake district and agricultural landscape.
-
A.
Datteln
Datteln is a town in North Rhine-Westphalia, Germany, known for its canal junction and industrial heritage.
-
B.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
C.
Dorsten
Dorsten is a town in North Rhine-Westphalia, Germany, located in the Ruhr area and known for its mix of industrial heritage and nearby natural landscapes.
-
D.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
E.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4f2a38c8190a682d8dae1ab9415 |
completed | April 10, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65e92f1808190a338d8406d651611 |
completed | May 2, 2026, 8:29 p.m. |
| NEDg | Description generation | batch_69f65fd6fa8c819094a31f8c2d8ee72d |
completed | May 2, 2026, 8:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f663fe2fac8190bb70c8f1b919d657 |
completed | May 2, 2026, 8:52 p.m. |
Created at: April 8, 2026, 9:41 p.m.