Triple
T13217680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leo Esaki |
E314661
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | IBM |
E1102
|
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: IBM | Statement: [Leo Esaki, employer, IBM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: IBM Context triple: [Leo Esaki, employer, IBM]
-
A.
IBM
chosen
IBM is a multinational technology and consulting company known for its pioneering work in computer hardware, software, and enterprise services.
-
B.
IBM AT
The IBM AT (Advanced Technology) is a mid-1980s IBM personal computer that introduced the 80286 processor and became a widely adopted standard for business PCs.
-
C.
Hewlett-Packard
Hewlett-Packard is a pioneering American technology company known for its innovations in computing, printers, and enterprise IT solutions.
-
D.
Computer Associates International
Computer Associates International (now known as CA Technologies) was a major American enterprise software company recognized for its broad portfolio of mainframe, security, and IT management solutions.
-
E.
Unisys
Unisys is an American global information technology company known for providing IT services, software, and infrastructure solutions to government and commercial clients.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf392e08190949ee4d194566395 |
completed | April 10, 2026, 11:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a31c8748190a24256a7dd1e346b |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:18 p.m.