Triple
T17633305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Leopold Lucas Prize |
E430030
|
entity |
| Predicate | hasNotableRecipient |
P108
|
FINISHED |
| Object | Jan Assmann |
—
|
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: Jan Assmann | Statement: [Dr. Leopold Lucas Prize, hasNotableRecipient, Jan Assmann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jan Assmann Context triple: [Dr. Leopold Lucas Prize, hasNotableRecipient, Jan Assmann]
-
A.
Jan Assmann
chosen
Jan Assmann is a German Egyptologist and cultural theorist renowned for his influential work on cultural memory and the history of religion.
-
B.
Aleida Assmann
Aleida Assmann is a German cultural scientist and literary scholar renowned for her influential work on cultural memory and remembrance.
-
C.
Geoffrey Lloyd
Geoffrey Lloyd was a British Conservative politician who served in several ministerial roles in the mid-20th century, particularly in areas related to energy and industry.
-
D.
Ali Harazim
Ali Harazim was a prominent Tijaniyya Sufi scholar and disciple known for systematizing and transmitting the teachings of Ahmad al-Tijani.
-
E.
Jean Sasson
Jean Sasson is an American author best known for her bestselling nonfiction books about women’s lives in the Middle East, including the "Princess" series.
- 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_69d889e37f308190a6aa0a69daff86c7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46ddea4688190a76a4225f268c0ac |
completed | April 19, 2026, 5:53 a.m. |
Created at: April 10, 2026, 5:52 a.m.