Triple
T13302290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eltersdorf |
E316841
|
entity |
| Predicate | hasNeighbouringDistrict |
P17964
|
FINISHED |
| Object | Tennenlohe |
E316840
|
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: Tennenlohe | Statement: [Eltersdorf, hasNeighbouringDistrict, Tennenlohe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tennenlohe Context triple: [Eltersdorf, hasNeighbouringDistrict, Tennenlohe]
-
A.
Tennenlohe
chosen
Tennenlohe is a district of Erlangen in Bavaria, Germany, known for its proximity to research institutions and the Tennenlohe Forest nature reserve.
-
B.
Fellhorn
Fellhorn is a prominent mountain in the Allgäu Alps on the German-Austrian border, popular for hiking and skiing near the town of Oberstdorf.
-
C.
Mittelhorn
Mittelhorn is a notable secondary summit in the Bernese Alps of Switzerland, forming part of the Wetterhorn massif.
-
D.
Nadelhorn
Nadelhorn is a prominent 4,000-meter-class peak in the Swiss Alps, known for its sharp, needle-like summit and popular alpine climbing routes.
-
E.
Berghaupten
Berghaupten is a small municipality in the Ortenau district of Baden-Württemberg in southwestern Germany, known for its scenic location in the Black Forest region.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990a60eb08190bf0dc098ca7dc342 |
completed | April 11, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716df8c2c8190bd17b47848546271 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:28 p.m.