Triple
T8695902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Disney Gift Card |
E206401
|
entity |
| Predicate | hasTermsAndConditions |
P34675
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Disney Gift Card, hasTermsAndConditions, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTermsAndConditions Context triple: [Disney Gift Card, hasTermsAndConditions, true]
-
A.
hasAgreementSystem
Indicates that there exists a specific grammatical agreement system (e.g., for features like number, gender, or person) associated with the given entity.
-
B.
hasTypeOfAgreement
Indicates that one entity is associated with, or governed by, a specific category or type of agreement.
-
C.
canEnterIntoAgreementsWith
Indicates that one entity has the legal or formal capacity to make binding agreements or contracts with another entity.
-
D.
hasAccord
Indicates that there is a formal agreement, harmony, or concord between the related entities.
-
E.
hasRegulations
chosen
Indicates that one entity imposes, contains, or is associated with rules or regulatory requirements that govern the behavior or operation of 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_69ca83555b6c8190abe930dd397e863b |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc582996a0819097ad03097ad1f103 |
completed | March 31, 2026, 11:26 p.m. |
| PD | Predicate disambiguation | batch_69cc456bda508190a9aa0fb92760739e |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:34 p.m.