Triple
T17493515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gersprenz |
E425988
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Babenhausen (Hesse) |
—
|
NE NERFINISHED |
How this triple was built (2 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: Babenhausen (Hesse) | Statement: [Gersprenz, flowsThrough, Babenhausen (Hesse)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Babenhausen (Hesse) Context triple: [Gersprenz, flowsThrough, Babenhausen (Hesse)]
-
A.
Babenhausen
Babenhausen is a market town in the Unterallgäu district of Bavaria, Germany, known for its historic center and role as a local administrative and service hub.
-
B.
Babenhausen
chosen
Babenhausen is a small town in the German state of Hesse, known for its historic old town and location southeast of Frankfurt am Main.
-
C.
Bebenhausen
Bebenhausen is a historic village in Baden-Württemberg, Germany, best known for its well-preserved former Cistercian monastery and royal hunting lodge.
-
D.
Borbach
Borbach is a small river or stream associated with the city of Witten in North Rhine-Westphalia, Germany.
-
E.
Essinghausen
Essinghausen is a village and locality that forms part of the town of Peine in Lower Saxony, Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451d6bd548190b4c6fae27c2a9ae8 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:48 a.m.