Triple
T11657494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sharp |
E277046
|
entity |
| Predicate | acquiredBy |
P347
|
FINISHED |
| Object | Foxconn |
E59361
|
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: Foxconn | Statement: [Sharp, acquiredBy, Foxconn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Foxconn Context triple: [Sharp, acquiredBy, Foxconn]
-
A.
Foxconn
chosen
Foxconn is a major Taiwanese multinational electronics manufacturer best known for assembling products for companies like Apple, including iPhones and other consumer devices.
-
B.
Huawei
Huawei is a major Chinese multinational technology company best known globally for its telecommunications equipment, smartphones, and role in 5G network infrastructure.
-
C.
Flextronics International
Flextronics International is a global electronics manufacturing services company that designs, builds, and services products for leading technology brands across various industries.
-
D.
Toshiba
Toshiba is a major Japanese multinational conglomerate known for its electronics, semiconductors, and information technology products and services.
-
E.
Apple Inc.
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.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a3d0331481909682b2e504e4c9a0 |
completed | April 10, 2026, 7:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef138faf4c81908043c71550048d75 |
completed | April 27, 2026, 7:43 a.m. |
Created at: April 8, 2026, 9:39 p.m.