Triple
T14613469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | COA |
E343016
|
entity |
| Predicate | operatorFrequentFlyerProgram |
P13481
|
FINISHED |
| Object | OnePass |
E343019
|
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: OnePass | Statement: [COA, operatorFrequentFlyerProgram, OnePass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OnePass Context triple: [COA, operatorFrequentFlyerProgram, OnePass]
-
A.
OnePass
chosen
OnePass was Continental Airlines’ frequent flyer loyalty program that allowed passengers to earn and redeem miles for flights and travel rewards.
-
B.
1Password
1Password is a popular cross-platform password manager that securely stores and autofills passwords, payment details, and other sensitive information for individuals and teams.
-
C.
LastPass
LastPass is a popular cloud-based password manager that securely stores and autofills users’ login credentials across devices.
-
D.
iPASS
iPASS is a Taiwanese contactless smart card widely used for public transportation fares and small-value electronic payments.
-
E.
SecurID
SecurID is a widely used two-factor authentication system that provides time-based one-time passwords via hardware or software tokens to enhance secure access to systems and data.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb45264988190a1df13e8b54a85bd |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda92110e88190af47b713dd24520b |
completed | May 8, 2026, 9:13 a.m. |
Created at: April 10, 2026, 1:25 a.m.