Triple
T3084401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Silverman |
E64334
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sarah |
E34678
|
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: Sarah | Statement: [Sarah Silverman, givenName, Sarah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Context triple: [Sarah Silverman, givenName, Sarah]
-
A.
Sarah
Sarah is the birth name of Margaret Fuller, the 19th-century American journalist, critic, and women's rights advocate associated with the Transcendentalist movement.
-
B.
Sarah
chosen
Sarah is a key matriarch in the Hebrew Bible, revered as the wife of Abraham and mother of Isaac in the Jewish, Christian, and Islamic traditions.
-
C.
Sarah
Sarah is the central protagonist of the story "Horse Girl," around whom the main narrative and character development revolve.
-
D.
Jessica
Jessica is a kind-hearted schoolteacher who becomes Mrs. Claus in the classic stop-motion Christmas special "Santa Claus Is Comin' to Town."
-
E.
Jessica
Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
- 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_69ad857bb4c88190a4cf27893fcabed8 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada1e98a1c8190b1dd4a0a47f7d6c6 |
completed | March 8, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2359ef3a88190a65f1f280d840bb0 |
completed | March 12, 2026, 3:40 a.m. |
Created at: March 8, 2026, 3:03 p.m.