Triple
T8410074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Narita Airport Terminal 2·3 Station |
E198599
|
entity |
| Predicate | servedByService |
P1294
|
FINISHED |
| Object | Skyliner |
E235302
|
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: Skyliner | Statement: [Narita Airport Terminal 2·3 Station, servedByService, Skyliner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skyliner Context triple: [Narita Airport Terminal 2·3 Station, servedByService, Skyliner]
-
A.
Keisei Skyliner
chosen
Keisei Skyliner is a high-speed limited express train service in Japan that links central Tokyo with Narita International Airport.
-
B.
Rokkō Liner
Rokkō Liner is an automated guideway transit line in Kobe, Japan, connecting the artificial island of Rokkō Island with the mainland.
-
C.
Thunderbird limited express
Thunderbird limited express is a Japanese limited express train service connecting major cities in the Kansai region with the Hokuriku area, known for its fast intercity travel and comfortable reserved seating.
-
D.
Tama Monorail
Tama Monorail is a straddle-beam monorail line in Tokyo, Japan, providing urban transit service through the Tama area.
-
E.
KL Monorail
KL Monorail is an elevated urban rail line in Kuala Lumpur that provides rapid transit service through the city’s central and commercial 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb8317045c8190b69cc99854b633be |
completed | March 31, 2026, 8:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce0317cb188190b207bcaffb629a75 |
completed | April 2, 2026, 5:48 a.m. |
Created at: March 30, 2026, 6:05 p.m.