Triple
T15645241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TRTS |
E376160
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | Circular Line |
E376166
|
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: Circular Line | Statement: [TRTS, hasLine, Circular Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Circular Line Context triple: [TRTS, hasLine, Circular Line]
-
A.
Circular line
chosen
The Circular line is a driverless rapid transit line in the Taipei Metro system that forms a loop connecting multiple districts around the city.
-
B.
Circular line
Circular line is the nickname for Line 6 of the Madrid Metro, a heavily used underground route that forms a loop around central Madrid.
-
C.
Circle Line
Circle Line is a mass rapid transit line in Singapore that forms a loop connecting key residential, commercial, and educational districts around the city.
-
D.
City Circle Line
The City Circle Line is a driverless rapid transit route in Copenhagen’s metro system that forms a loop connecting key districts and major transport hubs across the city.
-
E.
Big Circle Line
The Big Circle Line is a major circular metro line in the Moscow Metro system designed to connect outlying radial lines and reduce congestion in the city center.
- 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_69d85cd1564c8190991adda63bfab4b0 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ed400ec8190a14a9f7cf3092865 |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f4e558481909a39fdc5d104994a |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:15 a.m.