Triple
T22057049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gerhard Ertl |
E545046
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gerhard |
—
|
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: Gerhard | Statement: [Gerhard Ertl, givenName, Gerhard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gerhard Context triple: [Gerhard Ertl, givenName, Gerhard]
-
A.
Gerhard
chosen
Gerhard is a masculine given name of German origin, historically common in German-speaking countries.
-
B.
Wilhelm
Wilhelm is a Germanic given name, equivalent to William, historically borne by numerous European nobles, rulers, and notable figures.
-
C.
Lothar
Lothar is a masculine given name of Germanic origin, historically borne by various European nobles, military figures, and notable individuals.
-
D.
Hartmut
Hartmut is a masculine German given name, most notably borne by Nobel Prize–winning biochemist Hartmut Michel.
-
E.
Rudolf Christians
Rudolf Christians was a German stage and film actor active in the early 20th century, known for his work in both German and American productions.
- 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_69e11e3377c48190890c17407b9527d6 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128588e0081909056ac8251afe935 |
completed | April 28, 2026, 9:36 p.m. |
Created at: April 16, 2026, 8:27 p.m.