Triple
T5701916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zürich S-Bahn |
E125683
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
S19 line
The S19 line is a suburban rail service within the Zürich S-Bahn network that connects the city with surrounding regional destinations.
|
E562769
|
NE FINISHED |
How this triple was built (4 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: S19 line | Statement: [Zürich S-Bahn, hasPart, S19 line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S19 line Context triple: [Zürich S-Bahn, hasPart, S19 line]
-
A.
S9 line
The S9 line is a regional commuter rail service within the Zürich S-Bahn network, connecting Zürich with surrounding suburbs and towns.
-
B.
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.
-
C.
S11 line
The S11 line is a suburban rail service within the Zürich S-Bahn network that connects the city with surrounding regional destinations.
-
D.
S18 line
The S18 line is a regional commuter rail service in the Zürich S-Bahn network that connects the city with surrounding suburban areas.
-
E.
S2 line
The S2 line is a commuter rail service of the Zürich S-Bahn network that connects the city with surrounding suburban and regional areas.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: S19 line Triple: [Zürich S-Bahn, hasPart, S19 line]
Generated description
The S19 line is a suburban rail service within the Zürich S-Bahn network that connects the city with surrounding regional destinations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: S19 line Target entity description: The S19 line is a suburban rail service within the Zürich S-Bahn network that connects the city with surrounding regional destinations.
-
A.
S9 line
The S9 line is a regional commuter rail service within the Zürich S-Bahn network, connecting Zürich with surrounding suburbs and towns.
-
B.
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.
-
C.
S11 line
The S11 line is a suburban rail service within the Zürich S-Bahn network that connects the city with surrounding regional destinations.
-
D.
S18 line
The S18 line is a regional commuter rail service in the Zürich S-Bahn network that connects the city with surrounding suburban areas.
-
E.
S2 line
The S2 line is a commuter rail service of the Zürich S-Bahn network that connects the city with surrounding suburban and regional areas.
- F. None of above. chosen
Provenance (5 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_69c0082c96988190b3a6a201edce472a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0245581988190a819b8137533ed31 |
completed | March 22, 2026, 5:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c107cae1648190a4b3b4de602b5177 |
completed | March 23, 2026, 9:28 a.m. |
| NEDg | Description generation | batch_69c1090a7bac8190b5b9e003659b4b34 |
completed | March 23, 2026, 9:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c10d5102348190a9ec7421b1410a99 |
completed | March 23, 2026, 9:52 a.m. |
Created at: March 22, 2026, 3:45 p.m.