Triple
T8181146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Subte |
E191062
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | Line A |
E191064
|
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: Line A | Statement: [Subte, hasLine, Line A]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line A Context triple: [Subte, hasLine, Line A]
-
A.
Line A
Line A is a line of the Mexico City Metro system that serves the eastern part of the metropolitan area, connecting central Mexico City with several suburban municipalities.
-
B.
Line A
Line A is the main north–south rapid transit line of the Medellín Metro system in Colombia, serving as its busiest and most central corridor.
-
C.
Line A
Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
-
D.
Line A
chosen
Line A is the historic first subway line of the Buenos Aires Underground, known for its early 20th-century wooden cars and route through central neighborhoods.
-
E.
Line A
Line A is one of the main tram lines serving the city of Reims, France, providing urban public transportation across key districts.
- 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_69ca82c4538081909404325aa5639483 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4c4c2e388190b86854f8b1765e61 |
completed | March 31, 2026, 4:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbf86f8848190a196f7c8d3ad2b36 |
completed | April 1, 2026, 6:47 a.m. |
Created at: March 30, 2026, 5:40 p.m.