Triple
T12971629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hesse-Nassau |
E321410
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Bad Homburg vor der Höhe |
E193929
|
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 Homburg vor der Höhe | Statement: [Hesse-Nassau, contains, Bad Homburg vor der Höhe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Homburg vor der Höhe Context triple: [Hesse-Nassau, contains, Bad Homburg vor der Höhe]
-
A.
Homburg vor der Höhe
chosen
Homburg vor der Höhe is a historic spa town in the German state of Hesse, known for its mineral springs and the landmark Bad Homburg Castle.
-
B.
Bad Godesberg
Bad Godesberg is a district in the city of Bonn, Germany, known for its affluent residential areas, former diplomatic missions, and scenic location along the Rhine River.
-
C.
Bad Salzungen
Bad Salzungen is a spa town in Thuringia, Germany, known for its saline springs and therapeutic health resorts.
-
D.
Königswinter
Königswinter is a town on the right bank of the Rhine in North Rhine-Westphalia, Germany, known for the Drachenfels hill and its scenic location in the Siebengebirge range.
-
E.
Dinkelscherben
Dinkelscherben is a municipality in the Swabian region of Bavaria in southern Germany.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e418d548190be1c73db76cb3aa8 |
completed | April 10, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8ea91f08190b1daf6d05621acf9 |
completed | May 3, 2026, 2:54 a.m. |
Created at: April 9, 2026, 8:36 p.m.