Triple
T17308792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedrichshain-Kreuzberg |
E420234
|
entity |
| Predicate | hasPublicTransport |
P1288
|
FINISHED |
| Object | S9 line |
E698374
|
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: S9 line | Statement: [Friedrichshain-Kreuzberg, hasPublicTransport, S9 line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S9 line Context triple: [Friedrichshain-Kreuzberg, hasPublicTransport, S9 line]
-
A.
S9 line
The S9 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area and connecting central Frankfurt with surrounding suburbs and regional destinations.
-
B.
S9 line
The S9 line is a regional commuter rail service within the Zürich S-Bahn network, connecting Zürich with surrounding suburbs and towns.
-
C.
S9 line
chosen
The S9 line is a Berlin S-Bahn route that connects the city center with Berlin Brandenburg Airport and eastern districts, providing an important cross-city transit link.
-
D.
S9 Line
The S9 Line is a suburban rapid transit line of the Nanjing Metro system in Nanjing, China, connecting the urban area with outlying districts.
-
E.
S19 line
The S19 line is a suburban rail service within the Zürich S-Bahn network that connects the city with surrounding regional destinations.
- 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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43900eca88190930af0e4ec4fc0f9 |
completed | April 19, 2026, 2:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c440c58819084792fcd6b7a7a79 |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:43 a.m.