Triple
T55491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject |
E1096
|
entity | |
| Predicate | product |
P490
|
FINISHED |
| Object | Google Cloud Platform |
E1096
|
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: Google Cloud Platform | Statement: [Google, product, Google Cloud Platform]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google Cloud Platform Context triple: [Google, product, Google Cloud Platform]
-
A.
Google
chosen
Google is a multinational technology company best known for its search engine and wide range of internet-related products and services, including Android, YouTube, and cloud computing.
-
B.
IBM
IBM is a multinational technology and consulting company known for its pioneering work in computer hardware, software, and enterprise services.
-
C.
Microsoft
Microsoft is a multinational technology company best known for its Windows operating system, Office productivity suite, and Azure cloud computing platform.
-
D.
Tymshare
Tymshare was an influential American time-sharing and computer services company active in the 1960s–1980s that helped pioneer remote computing and software services for businesses.
-
E.
One Laptop per Child
One Laptop per Child is an educational nonprofit initiative that aimed to provide low-cost, durable laptops to children in developing countries to support digital learning and bridge the global digital divide.
- 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_69a248adc5b48190aa8db9fb092fb28a |
completed | Feb. 28, 2026, 1:45 a.m. |
| NER | Named-entity recognition | batch_69a24b06c5488190afb5429a7999e3f3 |
completed | Feb. 28, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a24e67187c8190950aec5d9f8ecc60 |
completed | Feb. 28, 2026, 2:09 a.m. |
Created at: Feb. 28, 2026, 1:50 a.m.