Triple
T7659053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karl Lagerfeld |
E173456
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Karl |
E79216
|
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: Karl | Statement: [Karl Lagerfeld, givenName, Karl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karl Context triple: [Karl Lagerfeld, givenName, Karl]
-
A.
Karl
Karl is the given name of German Field Marshal Gerd von Rundstedt, a prominent military leader during World War II.
-
B.
Karl
Karl is the given first name of Charles Proteus Steinmetz, the renowned German-American mathematician and electrical engineer who revolutionized the understanding of alternating current systems.
-
C.
Karl
Karl is the given name of Karl Popper, the influential 20th-century philosopher of science known for his theory of falsifiability.
-
D.
Karl
chosen
Karl is a Germanic given name, cognate with Charles, commonly used in German-speaking and other European countries.
-
E.
Karl
Karl is a ruthless, long-haired German terrorist and Hans Gruber’s vengeful right-hand man in the action film "Die Hard."
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701a32f588190a7a923e8ce43f727 |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8ac9527088190b3d7fd0987e66f35 |
completed | March 29, 2026, 4:37 a.m. |
Created at: March 27, 2026, 3:59 p.m.