Triple
T6071009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ems Dispatch crisis |
E135279
|
entity |
| Predicate | locationOfMeeting |
P50741
|
FINISHED |
| Object | Bad Ems |
E317369
|
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: Bad Ems | Statement: [Ems Dispatch crisis, locationOfMeeting, Bad Ems]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Ems Context triple: [Ems Dispatch crisis, locationOfMeeting, Bad Ems]
-
A.
Bad Ems
chosen
Bad Ems is a historic spa town in western Germany, renowned for its mineral springs and picturesque location along the Lahn River.
-
B.
Bad Mergentheim
Bad Mergentheim is a historic spa town in the German state of Baden-Württemberg, renowned for its mineral springs and picturesque setting in the Tauber Valley.
-
C.
Bad Tölz
Bad Tölz is a Bavarian spa town in southern Germany known for its historic old town, alpine scenery, and traditional German architecture.
-
D.
Bad Harzburg
Bad Harzburg is a German spa and resort town on the northern edge of the Harz Mountains, known for its thermal baths, hiking trails, and historic castle ruins.
-
E.
Bad Pyrmont
Bad Pyrmont is a German spa town in Lower Saxony renowned for its mineral springs, historic Kurpark, and long tradition as a health resort.
- 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_69c00879e8048190b690717d19c5bc03 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05758a21c81909cc10ef5f725a489 |
completed | March 22, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d349f288190833384eb9e6c7f52 |
completed | March 23, 2026, 11 a.m. |
Created at: March 22, 2026, 4:10 p.m.