Triple
T3715838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D Line |
E81527
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Purple Line |
E348411
|
NE FINISHED |
How this triple was built (2 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: Purple Line | Statement: [D Line, formerName, Purple Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Purple Line Context triple: [D Line, formerName, Purple Line]
-
A.
Purple Line
chosen
The Purple Line is a major rapid transit corridor of Bengaluru’s Namma Metro system, connecting key residential and commercial areas across the city.
-
B.
Purple Line
The Purple Line is a rapid transit route in Chicago's 'L' system that primarily serves the city's northern suburbs and connects them to the North Side of Chicago.
-
C.
Blue Line
The Blue Line is one of the main lines of the Lisbon Metro system, serving key central and northern areas of Portugal’s capital city.
-
D.
Blue Line
The Blue Line is one of the primary routes of the MetroLink light rail system serving the St. Louis metropolitan area.
-
E.
Blue Line
The Blue Line is one of the main rapid transit corridors of the Hyderabad Metro system in Hyderabad, India.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ad8b1a81588190b3f27a5483bb610e |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc9cf77dc819098979094172d82d1 |
completed | March 8, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b53fd9ce4081908dee7b37946e86ae |
completed | March 14, 2026, 11 a.m. |
Created at: March 8, 2026, 3:33 p.m.