Triple
T19796236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adrian (costume designer) |
E475547
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Adolph |
—
|
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: Adolph | Statement: [Adrian (costume designer), givenName, Adolph]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Adolph Context triple: [Adrian (costume designer), givenName, Adolph]
-
A.
Adolph
chosen
Adolph is a masculine given name of German origin, historically borne by various notable figures.
-
B.
Rudolf
Rudolf is the given name of the German Field Marshal Gerd von Rundstedt, a prominent military leader during World War II.
-
C.
Rudolf
Rudolf is a masculine given name of German origin, historically borne by several notable figures including scientists, nobles, and artists.
-
D.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
-
E.
Theodor
Theodor is the given name of Emil Theodor Kocher, a Swiss surgeon and Nobel laureate renowned for his pioneering work in thyroid surgery.
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653c723548190ac9bfaecaf8afb13 |
completed | April 20, 2026, 4:26 p.m. |
Created at: April 10, 2026, 1:49 p.m.