Triple
T20003237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | iPhone 6 |
E494387
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | iPhone |
—
|
NE NERFINISHED |
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: iPhone | Statement: [iPhone 6, brand, iPhone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: iPhone Context triple: [iPhone 6, brand, iPhone]
-
A.
iPhone
chosen
The iPhone is Apple's flagship smartphone line that revolutionized mobile technology by combining a touchscreen interface, internet connectivity, and a robust app ecosystem into a single device.
-
B.
iOS
iOS is Apple’s mobile operating system that powers iPhones and iPads, known for its integrated ecosystem, security features, and curated App Store.
-
C.
Ios
Ios is a Greek island in the Cyclades known for its picturesque whitewashed villages, sandy beaches, and vibrant nightlife.
-
D.
iPad
The iPad is Apple's line of touchscreen tablet computers that popularized modern tablet computing with its sleek design, intuitive interface, and integration into the broader Apple ecosystem.
-
E.
IOS
IOS is the abbreviation for the International Officer School, a U.S. Air Force education program that trains and develops international military officers.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a2e34481908a495cc5d077c41f |
completed | April 20, 2026, 5:25 p.m. |
Created at: April 11, 2026, 3:33 p.m.