Triple
T2777554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göttingen district |
E61609
|
entity |
| Predicate | hasRiver |
P165
|
FINISHED |
| Object | Werra |
E112665
|
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: Werra | Statement: [Göttingen district, hasRiver, Werra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Werra Context triple: [Göttingen district, hasRiver, Werra]
-
A.
Werra
chosen
The Werra is a major river in central Germany that forms one of the two headstreams of the Weser.
-
B.
Regnitz
The Regnitz is a river in the German state of Bavaria that flows through cities such as Erlangen and Bamberg before joining the Main River.
-
C.
Neckar
The Neckar is a significant river in southwestern Germany that flows through cities like Stuttgart and Heidelberg before joining the Rhine.
-
D.
Saale
The Saale is a major river in central Germany that flows through the states of Thuringia, Saxony-Anhalt, and Bavaria before joining the Elbe.
-
E.
Unstrut River
The Unstrut River is a tributary of the Saale in central Germany, flowing through Thuringia and Saxony-Anhalt and known for its scenic valleys, vineyards, and historic towns.
- 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_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd82a864819082bd1181a16d5208 |
completed | March 7, 2026, 8:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b367e000dc8190baba64c6e8ccf308 |
completed | March 13, 2026, 1:26 a.m. |
Created at: March 6, 2026, 9:57 p.m.