Triple
T13903341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hangzhou Metro |
E334282
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line 7
Line 7 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as part of the city's urban rail network.
|
E1068773
|
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 7 | Statement: [Hangzhou Metro, hasLine, Line 7]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 7 Context triple: [Hangzhou Metro, hasLine, Line 7]
-
A.
Line 7
Line 7 is a route of Mexico City’s Metrobús bus rapid transit system that serves key corridors with dedicated lanes and high-capacity articulated buses.
-
B.
Line 7
Line 7 is one of the main lines of the Tehran Metro rapid transit network, serving various districts of Iran’s capital city.
-
C.
Line 7
Line 7 is a rapid transit route of the STC Metro system, serving as one of its numbered lines within the network.
-
D.
Line 7
Line 7 is an east–west rapid transit line of the Beijing Subway serving several central and southwestern districts of Beijing.
-
E.
Line 7
Line 7 is a rapid transit line of the Guangzhou Metro system serving parts of Guangzhou and its surrounding 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: Line 7 Triple: [Hangzhou Metro, hasLine, Line 7]
Generated description
Line 7 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as part of the city's urban rail network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 7 Target entity description: Line 7 is a rapid transit line of the Hangzhou Metro system in Hangzhou, China, serving as part of the city's urban rail network.
-
A.
Line 7
Line 7 is a rapid transit line of the Guangzhou Metro system serving parts of Guangzhou and its surrounding areas.
-
B.
Line 7
Line 7 is a rapid transit route of the STC Metro system, serving as one of its numbered lines within the network.
-
C.
Line 7
Line 7 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving several key districts across the city.
-
D.
Line 7
Line 7 is a major rapid transit route of the Shanghai Metro that runs in a roughly north–south direction, connecting several key residential, commercial, and cultural areas across the city.
-
E.
Line 7
Line 7 is one of the main lines of the Tehran Metro rapid transit network, serving various districts of Iran’s capital city.
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de25db1e308190aaed6a21e443cc44 |
completed | April 14, 2026, 11:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c722e72081909090b2d64000ebd9 |
completed | May 3, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_69f7c83a3e04819097b6e0b5a3161b9a |
completed | May 3, 2026, 10:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7c9732d188190a8a7151d21e0a310 |
completed | May 3, 2026, 10:17 p.m. |
Created at: April 9, 2026, 10:16 p.m.