Triple
T2066442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toshiba |
E45910
|
entity |
| Predicate | competitor |
P1375
|
FINISHED |
| Object | Panasonic |
E84596
|
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: Panasonic | Statement: [Toshiba, competitor, Panasonic]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Panasonic Context triple: [Toshiba, competitor, Panasonic]
-
A.
Panasonic
chosen
Panasonic is a major Japanese multinational electronics company known for its wide range of consumer electronics, home appliances, and industrial solutions.
-
B.
Sharp Corporation
Sharp Corporation is a Japanese multinational electronics manufacturer known for its consumer electronics, display technologies, and home appliances.
-
C.
Toshiba
Toshiba is a major Japanese multinational conglomerate known for its electronics, semiconductors, and information technology products and services.
-
D.
LG Electronics
LG Electronics is a South Korean multinational electronics company known for producing a wide range of consumer electronics, home appliances, and mobile devices.
-
E.
Mitsubishi Electric
Mitsubishi Electric is a global Japanese electronics and electrical equipment manufacturer known for producing advanced technologies ranging from factory automation systems and power equipment to large-scale display and video board solutions.
- 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_69a8891b38288190abd572ccad9b6928 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9f236f08190b602d337afb1880f |
completed | March 7, 2026, 5:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae2722b5048190a78978a64167cda2 |
completed | March 9, 2026, 1:49 a.m. |
Created at: March 4, 2026, 7:40 p.m.