Triple
T9504675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | County of Leiningen |
E229232
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object |
Dagsburg
Dagsburg is a historic town in western Germany known for its medieval castle and role as a regional administrative center.
|
E803762
|
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: Dagsburg | Statement: [County of Leiningen, hasCapital, Dagsburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dagsburg Context triple: [County of Leiningen, hasCapital, Dagsburg]
-
A.
Spassburg
Spassburg is the German-themed area of the Six Flags Fiesta Texas amusement park, featuring rides, shops, and architecture inspired by traditional German towns.
-
B.
Doesburg
Doesburg is a historic city in the Dutch province of Gelderland, known for its well-preserved medieval center and location at the confluence of the IJssel and Oude IJssel rivers.
-
C.
Siegburg
Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
-
D.
Hemfurth
Hemfurth is a village in central Germany best known for its proximity to the historic Eder Dam and the Edersee reservoir.
-
E.
Osterburg
Osterburg is a small town in the German state of Saxony-Anhalt, known for its historic architecture and rural surroundings.
- 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: Dagsburg Triple: [County of Leiningen, hasCapital, Dagsburg]
Generated description
Dagsburg is a historic town in western Germany known for its medieval castle and role as a regional administrative center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dagsburg Target entity description: Dagsburg is a historic town in western Germany known for its medieval castle and role as a regional administrative center.
-
A.
Spassburg
Spassburg is the German-themed area of the Six Flags Fiesta Texas amusement park, featuring rides, shops, and architecture inspired by traditional German towns.
-
B.
Doesburg
Doesburg is a historic city in the Dutch province of Gelderland, known for its well-preserved medieval center and location at the confluence of the IJssel and Oude IJssel rivers.
-
C.
Siegburg
Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
-
D.
Hemfurth
Hemfurth is a village in central Germany best known for its proximity to the historic Eder Dam and the Edersee reservoir.
-
E.
Osterburg
Osterburg is a small town in the German state of Saxony-Anhalt, known for its historic architecture and rural surroundings.
- 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_69ca847611c48190a28c028644198c75 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9850fe6c8190a5a96cfae12562c6 |
completed | April 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d13a17abac8190823cec6b8328bc96 |
completed | April 4, 2026, 4:19 p.m. |
| NEDg | Description generation | batch_69d13be79b1c8190a9110312ae25cf32 |
completed | April 4, 2026, 4:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d13ca165b88190b4d629df0e079b3b |
completed | April 4, 2026, 4:30 p.m. |
Created at: March 30, 2026, 7:57 p.m.