Triple
T13489282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S-Bahn line S1 |
E318589
|
entity |
| Predicate | via |
P5680
|
FINISHED |
| Object | Nikolassee |
E104681
|
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: Nikolassee | Statement: [S-Bahn line S1, via, Nikolassee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nikolassee Context triple: [S-Bahn line S1, via, Nikolassee]
-
A.
Nikolassee
chosen
Nikolassee is a residential locality in southwestern Berlin known for its lakeside setting, green spaces, and villa-style neighborhoods.
-
B.
Tsitska
Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
-
C.
Nikitin
Nikitin is a Russian surname borne by numerous notable figures in fields such as art, science, and sports.
-
D.
Grusinskaya
Grusinskaya is a fading but still celebrated Russian ballerina whose loneliness and vulnerability are central to the drama of the film "Grand Hotel."
-
E.
Nikolayeva
Nikolayeva is a Russian surname most notably associated with the acclaimed Soviet pianist and composer Tatiana Nikolayeva.
- 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf3cbe2081908c6792362c67c8f1 |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7463b758c8190abc0dd2a049d751e |
completed | May 3, 2026, 12:57 p.m. |
Created at: April 9, 2026, 9:43 p.m.