Triple
T3686011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spandau |
E78225
|
entity |
| Predicate | servedBy |
P82
|
FINISHED |
| Object | Berlin S-Bahn |
E26713
|
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: Berlin S-Bahn | Statement: [Spandau, servedBy, Berlin S-Bahn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berlin S-Bahn Context triple: [Spandau, servedBy, Berlin S-Bahn]
-
A.
Berlin S-Bahn
chosen
The Berlin S-Bahn is a rapid transit railway network serving Berlin and its surrounding areas, integrating suburban and urban rail services across the metropolitan region.
-
B.
Berlin U-Bahn
The Berlin U-Bahn is the German capital’s extensive underground rapid transit system, forming a core part of its public transportation network.
-
C.
S-Bahn
The S-Bahn is a German urban and suburban rapid transit rail system that connects city centers with surrounding metropolitan regions.
-
D.
Frankfurt U-Bahn
The Frankfurt U-Bahn is the rapid transit system serving Frankfurt am Main, Germany, forming a core part of the city's public transportation network with multiple underground and surface lines.
-
E.
Munich U-Bahn
The Munich U-Bahn is the German city's rapid transit metro system, forming a core part of its public transportation network with multiple underground lines serving urban and suburban areas.
- 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_69ad85e285a081908f8cbfa9e2ed9b75 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4c676748190b074abfb9ba43b49 |
completed | March 8, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b57f0989c88190b4c675c7a6ec0e3e |
completed | March 14, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:26 p.m.