Triple
T10511920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gregorio Ricci-Curbastro |
E247935
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | Albert Einstein |
E318
|
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: Albert Einstein | Statement: [Gregorio Ricci-Curbastro, influenced, Albert Einstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Albert Einstein Context triple: [Gregorio Ricci-Curbastro, influenced, Albert Einstein]
-
A.
Albert Einstein
chosen
Albert Einstein was a theoretical physicist best known for developing the theory of relativity and fundamentally reshaping modern physics.
-
B.
Einstein
Einstein is a renowned surname most famously associated with theoretical physicist Albert Einstein, whose work revolutionized modern physics.
-
C.
Einstein
Einstein is the time-traveling sheepdog from the Back to the Future film series, known as Doc Brown’s beloved canine companion and the first test subject of the DeLorean time machine.
-
D.
Thomas Einstein
Thomas Einstein is a physician and anesthesiologist known for being a great-grandson of physicist Albert Einstein.
-
E.
EINSTEIN
EINSTEIN is a U.S. federal intrusion detection and prevention system used to monitor and protect government agency networks from cyber threats.
- 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509b5fcb8819087a23a2b26aecd70 |
completed | April 7, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dcf65f808190993dbacde2df20eb |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 6, 2026, 12:27 p.m.