Triple
T16398460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Color Line Arena |
E398246
|
entity |
| Predicate | namingRightsHolder |
P3318
|
FINISHED |
| Object | Color Line |
E1209634
|
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: Color Line | Statement: [Color Line Arena, namingRightsHolder, Color Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Color Line Context triple: [Color Line Arena, namingRightsHolder, Color Line]
-
A.
Color Line
chosen
Color Line is a Norwegian shipping company best known for operating ferry and cruise services connecting Norway with other European countries.
-
B.
Blue line
The Blue line is one of the main lines of the Stockholm metro system, connecting central Stockholm with several northern and western suburbs.
-
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.
Farben
Farben is the third movement of Arnold Schoenberg's Five Orchestral Pieces, Op. 16, noted for its innovative use of tone color and static harmonies.
-
E.
Dark Red Line
Dark Red Line is a mass rapid transit route known for its dark red color designation within an MRT system.
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e327cdc62481909de144b09a921e63 |
completed | April 18, 2026, 6:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c5bcc7c81909dc1d3a9a1b7f50a |
completed | May 10, 2026, 8:05 a.m. |
Created at: April 10, 2026, 5:09 a.m.