Triple
T17037428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle |
E413356
|
entity |
| Predicate | editorOfWork |
P107303
|
FINISHED |
| Object | Stefan Grube |
unclear NED1
|
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: Stefan Grube | Statement: [Michelle, editorOfWork, Stefan Grube]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stefan Grube Context triple: [Michelle, editorOfWork, Stefan Grube]
-
A.
Stefan Grube
Stefan Grube is a film editor best known for his work on the thriller "10 Cloverfield Lane."
-
B.
Stefan Grube
Stefan Grube is an editor known for his work on the film "Tully."
-
C.
Stefan Vogl
Stefan Vogl is an ice hockey player known for emerging from the development system of the German club ESV Kaufbeuren.
-
D.
Andreas Huber
Andreas Huber is a relatively common German-speaking personal name shared by multiple individuals across fields such as sports, engineering, and the arts.
-
E.
Markus Sattler
Markus Sattler is a German software engineer and entrepreneur best known as a co-founder and former CTO of the email marketing platform Mailjet.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d8f38b58819093af4054c3459726 |
completed | April 18, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012ed2ad708190a250762997611569 |
completed | May 11, 2026, 1:20 a.m. |
Created at: April 10, 2026, 5:33 a.m.