Triple
T2791131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara Sedgwick |
E61930
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sara |
E33933
|
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: Sara | Statement: [Sara Sedgwick, givenName, Sara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sara Context triple: [Sara Sedgwick, givenName, Sara]
-
A.
Sara
Sara is a language spoken in parts of Central Africa, particularly in Chad.
-
B.
Sara
chosen
Sara is a feminine given name of Hebrew origin meaning "princess," historically borne by notable figures including Sara Ann Delano Roosevelt, the mother of U.S. President Franklin D. Roosevelt.
-
C.
Samantha
Samantha is the middle name of the fictional socialite Tracy Samantha Lord from the classic film and play "The Philadelphia Story."
-
D.
Samantha
Samantha is an AI character, likely designed as a virtual persona with human-like conversational abilities and personality traits.
-
E.
Sandra
Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
- 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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddcfe19c81908f94dd449f18a2c2 |
completed | March 7, 2026, 8:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc65c1f848190b6efeefb64a3e131 |
completed | March 10, 2026, 7:21 a.m. |
Created at: March 6, 2026, 9:58 p.m.