Triple
T22923266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shamit Kachru |
E569220
|
entity |
| Predicate | hasNotableStudent |
P4838
|
FINISHED |
| Object | Eva Silverstein |
—
|
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: Eva Silverstein | Statement: [Shamit Kachru, hasNotableStudent, Eva Silverstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eva Silverstein Context triple: [Shamit Kachru, hasNotableStudent, Eva Silverstein]
-
A.
Eva Silverstein
chosen
Eva Silverstein is a theoretical physicist known for her influential work in string theory, cosmology, and quantum gravity.
-
B.
Elisha Applebaum
Elisha Applebaum is a British actress best known for playing Musa in the Netflix fantasy series "Fate: The Winx Saga."
-
C.
Diane Kagan
Diane Kagan is an American actress known for her supporting role in the 1990 drama film "Mr. and Mrs. Bridge."
-
D.
Anne Neuberger
Anne Neuberger is an American national security official known for her leadership in U.S. cybersecurity and technology policy at the highest levels of government.
-
E.
Susan B. Landau
Susan B. Landau is a film producer best known for her work on the popular 1993 sports comedy "Cool Runnings."
- 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_69e2458f7d008190901dccbaebeaba24 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f180d7973c8190b09a5690fd1d3f28 |
completed | April 29, 2026, 3:53 a.m. |
Created at: April 17, 2026, 3:43 p.m.