Triple
T12887131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darmstadt-Dieburg |
E308256
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Fischbachtal
Fischbachtal is a small municipality in southern Hesse, Germany, known for its rural landscape and the historic Lichtenberg Castle.
|
E1017759
|
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: Fischbachtal | Statement: [Darmstadt-Dieburg, contains, Fischbachtal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fischbachtal Context triple: [Darmstadt-Dieburg, contains, Fischbachtal]
-
A.
Urbachtal
Urbachtal is a remote alpine valley in the Bernese Oberland region of Switzerland, known for its rugged mountain scenery and hiking routes.
-
B.
Wohratal
Wohratal is a small rural municipality in the Marburg-Biedenkopf district of the German state of Hesse.
-
C.
Münstertal
Münstertal is a picturesque municipality in Germany’s Black Forest region, known for its scenic valley landscapes and traditional rural character.
-
D.
Weilersbach
Weilersbach is a small municipality in the Forchheim district of Bavaria, Germany, known for its rural character and proximity to the Franconian Switzerland region.
-
E.
Löstertal
Löstertal is a locality within the town of Wadern in the Saarland region of Germany, known for its rural character and scenic 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: Fischbachtal Triple: [Darmstadt-Dieburg, contains, Fischbachtal]
Generated description
Fischbachtal is a small municipality in southern Hesse, Germany, known for its rural landscape and the historic Lichtenberg Castle.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fischbachtal Target entity description: Fischbachtal is a small municipality in southern Hesse, Germany, known for its rural landscape and the historic Lichtenberg Castle.
-
A.
Urbachtal
Urbachtal is a remote alpine valley in the Bernese Oberland region of Switzerland, known for its rugged mountain scenery and hiking routes.
-
B.
Wohratal
Wohratal is a small rural municipality in the Marburg-Biedenkopf district of the German state of Hesse.
-
C.
Münstertal
Münstertal is a picturesque municipality in Germany’s Black Forest region, known for its scenic valley landscapes and traditional rural character.
-
D.
Weilersbach
Weilersbach is a small municipality in the Forchheim district of Bavaria, Germany, known for its rural character and proximity to the Franconian Switzerland region.
-
E.
Löstertal
Löstertal is a locality within the town of Wadern in the Saarland region of Germany, known for its rural character and scenic 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714415c08190aa9944b494a3ddad |
completed | April 10, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbbb199081909c32575097fbc2bf |
completed | May 3, 2026, 4:14 a.m. |
| NEDg | Description generation | batch_69f6cea882d48190add88a8463f7d544 |
completed | May 3, 2026, 4:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6cf72a0cc8190bf8b6d606b8d0987 |
completed | May 3, 2026, 4:30 a.m. |
Created at: April 9, 2026, 5:39 p.m.