Triple
T16578161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hartmannshof station |
E402766
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Hartmannshof |
E402766
|
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: Hartmannshof | Statement: [Hartmannshof station, locatedIn, Hartmannshof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hartmannshof Context triple: [Hartmannshof station, locatedIn, Hartmannshof]
-
A.
Hartmannshof
chosen
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
B.
Schickenhof
Schickenhof is a small locality in Germany best known as the birthplace of Nobel Prize–winning physicist Johannes Stark.
-
C.
Marienhof
Marienhof is a German television soap opera that gained popularity in the 1990s and 2000s for its portrayal of everyday life and relationships in a fictional Cologne neighborhood.
-
D.
Scheibenhof
Scheibenhof is a locality or district that forms part of the city of Krems an der Donau in Lower Austria.
-
E.
Heiderhof
Heiderhof is a residential subdistrict of the Bonn borough Bad Godesberg in western 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3595e9e1081909b220fb2de630348 |
completed | April 18, 2026, 10:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006eecbb6c81908abc5659333a4879 |
completed | May 10, 2026, 11:41 a.m. |
Created at: April 10, 2026, 5:16 a.m.