Triple
T3966034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S-Bahn Nuremberg |
E92218
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | S5 |
E142488
|
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: S5 | Statement: [S-Bahn Nuremberg, hasLine, S5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S5 Context triple: [S-Bahn Nuremberg, hasLine, S5]
-
A.
S5
chosen
S5 is a line of the Berlin S-Bahn rapid transit network serving routes between central Berlin and its eastern suburbs.
-
B.
S59
S59 is a Staten Island bus route in New York City that provides local transit service through neighborhoods including New Springville.
-
C.
S45
S45 is a Berlin S-Bahn suburban rail line that connects the city’s southern districts, including Berlin Brandenburg Airport, with the wider urban transit network.
-
D.
S550
S550 is Ford’s internal platform designation for the sixth-generation Mustang, introduced for the 2015 model year with a modernized chassis and global market focus.
-
E.
SA5
SA5 is the 3GPP Service and System Aspects working group responsible for management, orchestration, and operations of mobile communication networks.
- 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_69aed96624188190ac8c45bb57ab72b5 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef976f4fc8190b2c16ab62c19cdb8 |
completed | March 9, 2026, 4:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b533be4a688190a7d011ae2858e6ed |
completed | March 14, 2026, 10:09 a.m. |
Created at: March 9, 2026, 3:32 p.m.