Triple
T27835777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | エポスカード |
E703221
|
entity |
| Predicate | 優待内容 |
P100194
|
FINISHED |
| Object | 映画館での割引特典 |
—
|
LITERAL 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: 映画館での割引特典 | Statement: [エポスカード, 優待内容, 映画館での割引特典]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 優待内容 Context triple: [エポスカード, 優待内容, 映画館での割引特典]
-
A.
exclusiveBenefit
Indicates that a benefit is provided to one party or group in a way that excludes others from receiving the same advantage.
-
B.
restaurantBenefit
Indicates that one entity provides a benefit, advantage, or positive outcome to a restaurant.
-
C.
offersIncentive
chosen
Indicates that one entity provides a reward, benefit, or motivation to another entity to encourage a specific action or behavior.
-
D.
offersPass
Indicates that one entity provides or makes available a pass (such as a ticket, permit, or access credential) to another entity.
-
E.
benefitCharacteristic
Indicates that one entity possesses a quality or feature that provides an advantage, usefulness, or positive effect to another entity.
- F. None of above.
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_69ef840b94b08190950a4f77296938b2 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f643ed0b7481908cf25f3afec0a61d |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641def1e88190a05bf865ced78b23 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 5:59 p.m.