Triple
T1650410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guangzhou Metro |
E35677
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line 13
Line 13 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China.
|
E185777
|
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: Line 13 | Statement: [Guangzhou Metro, hasLine, Line 13]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 13 Context triple: [Guangzhou Metro, hasLine, Line 13]
-
A.
Line 13
Line 13 is a suburban loop line of the Beijing Subway that serves the northern part of the city and connects several major transfer stations.
-
B.
Line 14
Line 14 is a major rapid transit line of the Beijing Subway system that serves multiple key residential and commercial districts across the city.
-
C.
Line 12
Line 12 is a major Mexico City Metro route known for being one of the system’s newest and most modern lines, connecting southeastern districts across a long east–west corridor.
-
D.
Line 16
Line 16 is a rapid transit line of the Beijing Subway system serving parts of the city with modern, high-capacity metro service.
-
E.
Line 15
Line 15 is a rapid transit line of the Beijing Subway system serving northern parts of the city with both urban and suburban stations.
- 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: Line 13 Triple: [Guangzhou Metro, hasLine, Line 13]
Generated description
Line 13 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 13 Target entity description: Line 13 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China.
-
A.
Line 13
Line 13 is a suburban loop line of the Beijing Subway that serves the northern part of the city and connects several major transfer stations.
-
B.
Line 14
Line 14 is a major rapid transit line of the Beijing Subway system that serves multiple key residential and commercial districts across the city.
-
C.
Line 12
Line 12 is a major Mexico City Metro route known for being one of the system’s newest and most modern lines, connecting southeastern districts across a long east–west corridor.
-
D.
Line 16
Line 16 is a rapid transit line of the Beijing Subway system serving parts of the city with modern, high-capacity metro service.
-
E.
Line 15
Line 15 is a rapid transit line of the Beijing Subway system serving northern parts of the city with both urban and suburban stations.
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a66b58c819082d38ef1c805cf44 |
completed | March 5, 2026, 4:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60a996508190bc227400cb7713ac |
completed | March 8, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_69ad61323b308190b883c4bf2c3ca1bf |
completed | March 8, 2026, 11:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad622d695481909351a9c80f8d646f |
completed | March 8, 2026, 11:49 a.m. |
Created at: March 4, 2026, 7:29 p.m.