Triple
T7842497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yellow Line (Delhi Metro) |
E181839
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Model Town
Model Town is a residential neighborhood in North Delhi, India, known for its planned layout and connectivity via the Delhi Metro.
|
E698906
|
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: Model Town | Statement: [Yellow Line (Delhi Metro), hasStation, Model Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Model Town Context triple: [Yellow Line (Delhi Metro), hasStation, Model Town]
-
A.
Toy Town
Toy Town is the colorful, whimsical village setting in Enid Blyton’s Noddy stories, inhabited by living toys and other playful characters.
-
B.
Mytown
Mytown was an Irish boy band from the late 1990s that featured future The Script frontman Danny O’Donoghue.
-
C.
The Model City
The Model City is the nickname of Anniston, Alabama, reflecting its origins as a carefully planned industrial community in the late 19th century.
-
D.
Modelland
Modelland is a young adult fantasy novel by supermodel Tyra Banks that satirically explores the world of modeling through a magical, dystopian academy.
-
E.
Twin Town
"Twin Town" is a darkly comic 1997 Welsh crime film set in Swansea, known for its irreverent tone and starring Rhys Ifans in one of his early breakout roles.
- 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: Model Town Triple: [Yellow Line (Delhi Metro), hasStation, Model Town]
Generated description
Model Town is a residential neighborhood in North Delhi, India, known for its planned layout and connectivity via the Delhi Metro.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Model Town Target entity description: Model Town is a residential neighborhood in North Delhi, India, known for its planned layout and connectivity via the Delhi Metro.
-
A.
Toy Town
Toy Town is the colorful, whimsical village setting in Enid Blyton’s Noddy stories, inhabited by living toys and other playful characters.
-
B.
Mytown
Mytown was an Irish boy band from the late 1990s that featured future The Script frontman Danny O’Donoghue.
-
C.
The Model City
The Model City is the nickname of Anniston, Alabama, reflecting its origins as a carefully planned industrial community in the late 19th century.
-
D.
Modelland
Modelland is a young adult fantasy novel by supermodel Tyra Banks that satirically explores the world of modeling through a magical, dystopian academy.
-
E.
Twin Town
"Twin Town" is a darkly comic 1997 Welsh crime film set in Swansea, known for its irreverent tone and starring Rhys Ifans in one of his early breakout roles.
- 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_69ca8285d6488190a95d4c02d7354b53 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb163b32688190b463a9cd8fa3c690 |
completed | March 31, 2026, 12:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5aded4048190b18604963784352c |
completed | March 31, 2026, 5:25 a.m. |
| NEDg | Description generation | batch_69cb762dd8348190bf74be4e7f5df1e7 |
completed | March 31, 2026, 7:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cbb24068908190977b266366e5ceea |
completed | March 31, 2026, 11:38 a.m. |
Created at: March 30, 2026, 4:48 p.m.