Triple
T321671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | macOS |
E6427
|
entity |
| Predicate | owner |
P347
|
FINISHED |
| Object | Apple Inc. |
E3002
|
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: Apple Inc. | Statement: [macOS, owner, Apple Inc.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apple Inc. Context triple: [macOS, owner, Apple Inc.]
-
A.
Apple Inc.
chosen
Apple Inc. is a multinational technology company best known for designing and selling consumer electronics like the iPhone, Mac, and iPad, along with software and digital services.
-
B.
NeXT Inc.
NeXT Inc. was a computer company founded by Steve Jobs that developed advanced workstations and the NeXTSTEP operating system, which later formed the technological foundation for macOS and iOS.
-
C.
Hewlett-Packard
Hewlett-Packard is a pioneering American technology company known for its innovations in computing, printers, and enterprise IT solutions.
-
D.
IBM
IBM is a multinational technology and consulting company known for its pioneering work in computer hardware, software, and enterprise services.
-
E.
Micros Systems
Micros Systems was a leading provider of point-of-sale and hospitality management software and hardware solutions for restaurants, hotels, and retail businesses.
- 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_69a2e7933d6c8190bb2592ad13286ef2 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea81a1e88190b3496070eb3d85f5 |
completed | Feb. 28, 2026, 1:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3e5715a0c8190ad1333d8aaaecc1b |
completed | March 1, 2026, 7:06 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.