Triple
T7384718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanjing Metro |
E170351
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
S1 Line
The S1 Line is a rapid transit route within the Nanjing Metro system in Nanjing, China, providing urban rail service along one of the city’s key corridors.
|
E660753
|
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: S1 Line | Statement: [Nanjing Metro, hasLine, S1 Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S1 Line Context triple: [Nanjing Metro, hasLine, S1 Line]
-
A.
S1 Line
The S1 Line is a medium-capacity maglev rapid transit line in the Beijing Subway system serving the western suburbs of the city.
-
B.
S1 line
The S1 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area and surrounding region.
-
C.
Riverside Line
The Riverside Line is a Metrolink commuter rail route in Southern California that connects Los Angeles with cities in the Inland Empire, including Riverside.
-
D.
S2 line
The S2 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area, including central hubs such as Frankfurt Hauptwache station.
-
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: S1 Line Triple: [Nanjing Metro, hasLine, S1 Line]
Generated description
The S1 Line is a rapid transit route within the Nanjing Metro system in Nanjing, China, providing urban rail service along one of the city’s key corridors.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: S1 Line Target entity description: The S1 Line is a rapid transit route within the Nanjing Metro system in Nanjing, China, providing urban rail service along one of the city’s key corridors.
-
A.
S1 Line
The S1 Line is a medium-capacity maglev rapid transit line in the Beijing Subway system serving the western suburbs of the city.
-
B.
S1 line
The S1 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area and surrounding region.
-
C.
Riverside Line
The Riverside Line is a Metrolink commuter rail route in Southern California that connects Los Angeles with cities in the Inland Empire, including Riverside.
-
D.
S2 line
The S2 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area, including central hubs such as Frankfurt Hauptwache station.
-
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_69c68a5d0ed08190b6d361e68f813330 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1efe1308190b96eefbff56140be |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802e23714819094a1b31c82a27fee |
completed | March 28, 2026, 4:33 p.m. |
| NEDg | Description generation | batch_69c8038127408190947cb7002ccc0dec |
completed | March 28, 2026, 4:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8040a40088190b37192429678fd3e |
completed | March 28, 2026, 4:38 p.m. |
Created at: March 27, 2026, 3:08 p.m.