Triple
T954328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clement |
E20592
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Klemens |
E111656
|
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: Klemens | Statement: [Clement, hasVariant, Klemens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klemens Context triple: [Clement, hasVariant, Klemens]
-
A.
Klemens
chosen
Klemens is a given name, primarily used in German-speaking and Central European countries, that corresponds to the Latin-derived name Clemens.
-
B.
Nikolaus
Nikolaus is the traditional German figure based on Saint Nicholas who brings small gifts to children on the eve of December 6th.
-
C.
Hans Kraly
Hans Kraly was a screenwriter active during the silent and early sound film era, known for contributing to notable early 20th-century cinema.
-
D.
Gerhard
Gerhard is a masculine given name of German origin, historically common in German-speaking countries.
-
E.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3da8d508190b56b29d7f235d2c4 |
completed | March 1, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad1c8c828481909013039009446dc0 |
completed | March 8, 2026, 6:51 a.m. |
Created at: March 1, 2026, 7:40 p.m.