Triple
T19741631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mimi Rogers |
E474135
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Bosch |
—
|
NE NERFINISHED |
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: [Mimi Rogers, notableWork, Bosch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bosch Context triple: [Mimi Rogers, notableWork, Bosch]
-
A.
Bosch
Bosch is a multinational engineering and technology company best known for its automotive components, industrial products, and household appliances.
-
B.
Bosch
chosen
Bosch is a critically acclaimed American crime drama television series centered on LAPD detective Harry Bosch, adapted from Michael Connelly’s bestselling novels.
-
C.
Mann-Grundig
Mann-Grundig was a professional cycling team active in the 1960s and 1970s, known for competing in major European road races.
-
D.
Siemens
Siemens is a major German multinational conglomerate best known for its leading roles in industrial manufacturing, energy, healthcare technology, and infrastructure solutions worldwide.
-
E.
Mahle GmbH
Mahle GmbH is a German automotive parts manufacturer known worldwide for producing engine components, filtration systems, and thermal management solutions for vehicles.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65162382081909bd5a251d7da7f75 |
completed | April 20, 2026, 4:16 p.m. |
Created at: April 10, 2026, 1:47 p.m.