Triple
T15596735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ueffelner Aue |
E374911
|
entity |
| Predicate | tributaryOf |
P415
|
FINISHED |
| Object | Hase |
E77450
|
NE FINISHED |
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: Hase | Statement: [Ueffelner Aue, tributaryOf, Hase]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hase Context triple: [Ueffelner Aue, tributaryOf, Hase]
-
A.
Hase
chosen
The Hase is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia, passing towns such as Quakenbrück before joining the Ems.
-
B.
Haise
Haise is the surname of Fred Haise, the American astronaut and Apollo 13 lunar module pilot.
-
C.
Harku
Harku is a small settlement in northern Estonia located within Harku Parish, near the capital city of Tallinn.
-
D.
Hama
Hama is a major city in west-central Syria, historically known for its ancient waterwheels (norias) on the Orontes River and its role as an important agricultural and industrial center.
-
E.
Haselünne
Haselünne is a small historic town in Lower Saxony, Germany, known for its traditional grain distilleries and picturesque setting along the Hase River.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cce25008190b13b52745fbd719b |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e5f9db8819083abf80f01f32b3d |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f355ff48190a2c2c262c09e6de0 |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:12 a.m.