Triple
T8751591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massa |
E207971
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Bad Kissingen |
E567840
|
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 Kissingen | Statement: [Massa, twinnedWith, Bad Kissingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Kissingen Context triple: [Massa, twinnedWith, Bad Kissingen]
-
A.
Bad Kissingen
chosen
Bad Kissingen is a historic spa town in northern Bavaria, Germany, renowned for its mineral springs and 19th-century wellness resorts.
-
B.
Gauting
Gauting is a municipality in the district of Starnberg in Bavaria, Germany, known for its residential character and proximity to Munich.
-
C.
Backnang
Backnang is a town in the German state of Baden-Württemberg, located northeast of Stuttgart and known for its historical center and role as a regional industrial and commuter hub.
-
D.
Ingolstadt
Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
-
E.
Fürstenfeldbruck
Fürstenfeldbruck is a town in Upper Bavaria, Germany, known for its historic monastery, proximity to Munich, and nearby air base.
- 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_69ca835bb2bc819084bb5906cb6ef7f8 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5da774f4819099e5bfd12973d946 |
completed | March 31, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc91f45948190aeaab61c8a7dfc3c |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:39 p.m.