Triple
T15080375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weiße Elster |
E380123
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Halle (Saale) |
E94413
|
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: Halle (Saale) | Statement: [Weiße Elster, flowsThrough, Halle (Saale)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halle (Saale) Context triple: [Weiße Elster, flowsThrough, Halle (Saale)]
-
A.
Halle (Saale)
chosen
Halle (Saale) is a major city in the German state of Saxony-Anhalt, known as an important economic, cultural, and educational center, including being home to the Martin Luther University of Halle-Wittenberg.
-
B.
Haldensleben
Haldensleben is a town in the German state of Saxony-Anhalt, known as an administrative and economic center with historical roots dating back to the Middle Ages.
-
C.
Sangerhausen
Sangerhausen is a town in the German state of Saxony-Anhalt, known for its historic mining heritage and its renowned Europa-Rosarium rose garden.
-
D.
Halle
Halle is a feminine given name used in various cultures, notably borne by American actress and singer Halle Bailey.
-
E.
Halle
Halle is a surname most notably borne by Morris Halle, a prominent linguist and phonologist.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dff80008c88190840f94222f867478 |
completed | April 15, 2026, 8:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feb7dfae748190a0d59df5cbd5787d |
completed | May 9, 2026, 4:28 a.m. |
Created at: April 10, 2026, 3:03 a.m.