Triple
T14860114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deutsch–Jozsa algorithm |
E349464
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Deutsch problem |
E996066
|
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: Deutsch problem | Statement: [Deutsch–Jozsa algorithm, relatedTo, Deutsch problem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deutsch problem Context triple: [Deutsch–Jozsa algorithm, relatedTo, Deutsch problem]
-
A.
Deutch
Deutch is a surname most notably associated with John M. Deutch, an American chemist, academic, and former Director of Central Intelligence.
-
B.
Deutsch
chosen
Deutsch is a surname of German origin borne by numerous individuals across various fields, including arts, sciences, and public life.
-
C.
GRMN
GRMN is a high-performance sub-brand of Toyota’s Gazoo Racing division, offering limited-run, track-focused versions of select Toyota models.
-
D.
Deutsche
Deutsche is a German term meaning "German," commonly used in the names of German institutions, companies, and cultural entities.
-
E.
DEU
DEU is the three-letter ISO 3166-1 alpha-3 country code representing Germany.
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded44598e48190b759a05ed2d9ecaf |
completed | April 14, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe650a43bc8190b836fe690d2a3c71 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:54 a.m.