Triple
T15379972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juliet Landau |
E367772
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Bosch |
E346973
|
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: [Juliet Landau, notableWork, Bosch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bosch Context triple: [Juliet Landau, 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.
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.
Mahle GmbH
Mahle GmbH is a German automotive parts manufacturer known worldwide for producing engine components, filtration systems, and thermal management solutions for vehicles.
-
E.
Buderus
Buderus is a surname most notably associated with former Australian rugby league player Danny Buderus.
- 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_69d85a1551a08190ba2caea7cd51c639 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e6044488190b0499db109f7f821 |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b5996408190afab2221d38e0027 |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:19 a.m.