Triple
T573290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osaka Metro |
E13707
|
entity |
| Predicate | uses |
P98
|
FINISHED |
| Object |
ICOCA
ICOCA is a rechargeable contactless smart card used for fare payment on public transportation systems in the Kansai region of Japan.
|
E71900
|
NE FINISHED |
How this triple was built (4 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: ICOCA | Statement: [Osaka Metro, uses, ICOCA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ICOCA Context triple: [Osaka Metro, uses, ICOCA]
-
A.
ORCA card
The ORCA card is a reusable, contactless smart card used to pay fares across multiple public transit systems in the Puget Sound region of Washington State.
-
B.
Presto card
The Presto card is a reloadable smart card used for paying public transit fares across the Greater Toronto and Hamilton Area and other regions in Ontario, Canada.
-
C.
TAP card
The TAP card is a reusable contactless smart card used to pay fares across public transit systems in the Los Angeles County region.
-
D.
Clipper card
The Clipper card is a reloadable contactless smart card used to pay fares across multiple public transit systems in the San Francisco Bay Area.
-
E.
MetroCard
MetroCard is a magnetic stripe payment card formerly used as the primary method for paying fares on New York City’s public transit system, including subways and buses.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ICOCA Triple: [Osaka Metro, uses, ICOCA]
Generated description
ICOCA is a rechargeable contactless smart card used for fare payment on public transportation systems in the Kansai region of Japan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ICOCA Target entity description: ICOCA is a rechargeable contactless smart card used for fare payment on public transportation systems in the Kansai region of Japan.
-
A.
ORCA card
The ORCA card is a reusable, contactless smart card used to pay fares across multiple public transit systems in the Puget Sound region of Washington State.
-
B.
Presto card
The Presto card is a reloadable smart card used for paying public transit fares across the Greater Toronto and Hamilton Area and other regions in Ontario, Canada.
-
C.
TAP card
The TAP card is a reusable contactless smart card used to pay fares across public transit systems in the Los Angeles County region.
-
D.
Clipper card
The Clipper card is a reloadable contactless smart card used to pay fares across multiple public transit systems in the San Francisco Bay Area.
-
E.
MetroCard
MetroCard is a magnetic stripe payment card formerly used as the primary method for paying fares on New York City’s public transit system, including subways and buses.
- F. None of above. chosen
Provenance (5 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b4ae0988190bdd0ad428b784d85 |
completed | March 1, 2026, 8:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4ff4c3df48190a886a8d3c633d417 |
completed | March 2, 2026, 3:09 a.m. |
| NEDg | Description generation | batch_69a4ffcad6e08190938018ade5bc5d67 |
completed | March 2, 2026, 3:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5001de9c481909d43c001028c922c |
completed | March 2, 2026, 3:12 a.m. |
Created at: March 1, 2026, 7:33 p.m.