Triple
T23064094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tachibana Station |
E574983
|
entity |
| Predicate | ticketingSystem |
P3383
|
FINISHED |
| Object | PiTaPa |
—
|
NE NERFINISHED |
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: PiTaPa | Statement: [Tachibana Station, ticketingSystem, PiTaPa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PiTaPa Context triple: [Tachibana Station, ticketingSystem, PiTaPa]
-
A.
PiTaPa
chosen
PiTaPa is a rechargeable contactless smart card system used for fare payment on public transportation networks in the Kansai region of Japan.
-
B.
Pesa
Pesa is a Polish manufacturer of rail vehicles, particularly known for producing modern trams and trains used in various European cities.
-
C.
Pesa
The Pesa is a river in Tuscany, central Italy, known for flowing through the Chianti region before joining the Arno.
-
D.
Takas
Takas is a dialect of the Mwaghavul language spoken by a subgroup of the Mwaghavul people in Nigeria’s Plateau State.
-
E.
Lianlian
Lianlian is one of the three robot-themed mascots representing the 19th Asian Games held in Hangzhou, China, symbolizing the city’s cultural heritage and technological innovation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a1f49c81909db7e0473ec2bb1b |
completed | April 29, 2026, 4:31 a.m. |
Created at: April 17, 2026, 3:55 p.m.