Triple
T8830301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RTA Rail |
E210119
|
entity |
| Predicate | hasService |
P182
|
FINISHED |
| Object | Red Line |
E151856
|
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: Red Line | Statement: [RTA Rail, hasService, Red Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Red Line Context triple: [RTA Rail, hasService, Red Line]
-
A.
Red Line
chosen
Red Line is one of the main rapid transit routes of the Dubai Metro, running along key areas of the city and serving many of its major commercial and residential districts.
-
B.
Red Line
The Red Line is one of the major corridors of the Delhi Metro rapid transit system, serving numerous densely populated areas in and around Delhi.
-
C.
Red Line
The Red Line is a major light rail route in the Dallas Area Rapid Transit (DART) system serving key corridors across the Dallas–Fort Worth metro area.
-
D.
Red Line
The Red Line is a primary route of the MetroLink light rail system serving key destinations in the St. Louis metropolitan area.
-
E.
Red Line
Red 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_69ca8365b28081909e48e45e95dfc405 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc604db0788190a3082467d80fdaf5 |
completed | April 1, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf8921a13081909346b97c024110b6 |
completed | April 3, 2026, 9:32 a.m. |
Created at: March 30, 2026, 6:47 p.m.