Triple
T7394096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Intuit |
E170576
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object | Mint |
E662029
|
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: Mint | Statement: [Intuit, hasBrand, Mint]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mint Context triple: [Intuit, hasBrand, Mint]
-
A.
Mint
chosen
Mint is a popular personal finance management service and app that helps users track spending, budgets, and financial accounts in one place.
-
B.
Lima Mint
Lima Mint was a major Spanish colonial mint in Lima, Peru, known for producing silver coins such as the famous pieces of eight during the colonial era.
-
C.
Margarites
Margarites is a genus of small marine snails, commonly known as margarite top shells, found in cold and temperate seas.
-
D.
The Mint
The Mint is a posthumously published autobiographical work by T. E. Lawrence that candidly chronicles his experiences and observations while serving as an enlisted airman in the Royal Air Force.
-
E.
Doublemint
Doublemint is a popular Wrigley chewing gum brand known for its long-lasting mint flavor and iconic twin-themed advertising.
- 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_69c68a5f04188190ac266569c9280347 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2263b48819089319a2a2f0d3357 |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81ecf65388190a149efc77aedcd91 |
completed | March 28, 2026, 6:32 p.m. |
Created at: March 27, 2026, 3:09 p.m.