Triple
T16265295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noida Metro |
E394859
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Aqua Line
Aqua Line is a rapid transit corridor of the Noida Metro system serving key areas of Noida and Greater Noida in the Delhi National Capital Region.
|
E1203752
|
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: Aqua Line | Statement: [Noida Metro, hasLine, Aqua Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aqua Line Context triple: [Noida Metro, hasLine, Aqua Line]
-
A.
Aqua Line
Aqua Line is one of the main corridors of the Nagpur Metro rapid transit system in Nagpur, India.
-
B.
Harbour Line
Harbour Line is a major corridor of Mumbai's suburban railway network that connects the city’s eastern waterfront and Navi Mumbai suburbs to key central and southern Mumbai terminals.
-
C.
Harbour Line
Harbour Line is a Copenhagen Metro route that serves areas along the city’s waterfront and harbor districts.
-
D.
W Line
The W Line is a light rail route in the Denver metropolitan area that connects downtown Denver with the western suburbs as part of the Regional Transportation District (RTD) system.
-
E.
Evergreen Line
Evergreen Line is a major Taiwanese container shipping company known for operating a large global fleet and extensive international trade routes.
- 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: Aqua Line Triple: [Noida Metro, hasLine, Aqua Line]
Generated description
Aqua Line is a rapid transit corridor of the Noida Metro system serving key areas of Noida and Greater Noida in the Delhi National Capital Region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aqua Line Target entity description: Aqua Line is a rapid transit corridor of the Noida Metro system serving key areas of Noida and Greater Noida in the Delhi National Capital Region.
-
A.
Aqua Line
Aqua Line is one of the main corridors of the Nagpur Metro rapid transit system in Nagpur, India.
-
B.
Harbour Line
Harbour Line is a major corridor of Mumbai's suburban railway network that connects the city’s eastern waterfront and Navi Mumbai suburbs to key central and southern Mumbai terminals.
-
C.
Harbour Line
Harbour Line is a Copenhagen Metro route that serves areas along the city’s waterfront and harbor districts.
-
D.
W Line
The W Line is a light rail route in the Denver metropolitan area that connects downtown Denver with the western suburbs as part of the Regional Transportation District (RTD) system.
-
E.
Evergreen Line
Evergreen Line is a major Taiwanese container shipping company known for operating a large global fleet and extensive international trade routes.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245c73944819085633e6d2a69bae9 |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017b877088190893a1f012e5d2463 |
completed | May 10, 2026, 5:29 a.m. |
| NEDg | Description generation | batch_6a00183849bc8190a1896d240d8f91f0 |
completed | May 10, 2026, 5:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00190887088190a5a0eb2cfd674c98 |
completed | May 10, 2026, 5:35 a.m. |
Created at: April 10, 2026, 5:05 a.m.