Triple
T9813639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saar River |
E238341
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Saarland |
E36080
|
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: Saarland | Statement: [Saar River, flowsThrough, Saarland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saarland Context triple: [Saar River, flowsThrough, Saarland]
-
A.
Saarland
chosen
Saarland is a small federal state in southwestern Germany known for its industrial history, Franco-German cultural influences, and location along the borders with France and Luxembourg.
-
B.
Rhineland-Palatinate
Rhineland-Palatinate is a federal state in western Germany known for its wine-growing regions along the Rhine and Moselle rivers and its historic cities such as Mainz and Trier.
-
C.
Bavaria
Bavaria is a historic region and federal state in southeastern Germany, known for its distinct cultural traditions, large size and population, and major cities such as Munich.
-
D.
Pfalz
Pfalz is a major wine-producing region in southwestern Germany known for its diverse vineyards and high-quality white wines.
-
E.
Alsacia
Alsacia is a Madrid Metro station on Line 2 serving the San Blas-Canillejas district in eastern Madrid, Spain.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb22410208190b82b81a4df800f80 |
completed | April 2, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d257601eec8190b7fa205cee61bb23 |
completed | April 5, 2026, 12:36 p.m. |
Created at: March 30, 2026, 8:30 p.m.