Triple
T7388287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graphcore |
E170436
|
entity |
| Predicate | notableInvestor |
P27018
|
FINISHED |
| Object | Bosch |
E151289
|
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: Bosch | Statement: [Graphcore, notableInvestor, Bosch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bosch Context triple: [Graphcore, notableInvestor, Bosch]
-
A.
Bosch
chosen
Bosch is a multinational engineering and technology company best known for its automotive components, industrial products, and household appliances.
-
B.
Bosch
Bosch is a critically acclaimed American crime drama television series centered on LAPD detective Harry Bosch, adapted from Michael Connelly’s bestselling novels.
-
C.
Siemens
Siemens is a major German multinational conglomerate best known for its leading roles in industrial manufacturing, energy, healthcare technology, and infrastructure solutions worldwide.
-
D.
Eaton
Eaton is a surname most notably associated with American decathlete and Olympic gold medalist Ashton Eaton.
-
E.
Eaton
Eaton is the namesake of the Eaton Professor of the Science of Government at Harvard University, an endowed academic chair in political science and government studies.
- 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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1f3f5f48190aabe69ba79cbcb93 |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802e56fb48190976612d2a94d6ee5 |
completed | March 28, 2026, 4:33 p.m. |
Created at: March 27, 2026, 3:09 p.m.