Triple
T8068437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sylvia Ashley |
E188303
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sylvia |
E30938
|
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: Sylvia | Statement: [Sylvia Ashley, givenName, Sylvia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sylvia Context triple: [Sylvia Ashley, givenName, Sylvia]
-
A.
Sylvia
chosen
Sylvia is a feminine given name of Latin origin meaning "from the forest" or "of the woods."
-
B.
Sylvia
Sylvia is a key character in the film "The Truman Show," a woman who tries to reveal the truth to Truman about his manufactured reality and becomes his inspiration to escape.
-
C.
Sylvia
"Sylvia" is a biographical drama film about poet Sylvia Plath, focusing on her turbulent marriage to Ted Hughes and her creative and emotional struggles.
-
D.
Sylvia’s
Sylvia’s is a famed soul food restaurant in Harlem, New York City, renowned for its Southern cuisine and cultural significance in the neighborhood.
-
E.
Muriel
Muriel is a feminine given name of French origin that has been borne by various notable figures, including politicians, writers, and artists.
- 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_69ca82b42674819086840efea12478e5 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3ff8a4fc8190a97fc7111ca7ec4d |
completed | March 31, 2026, 3:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63e587908190980f1f026f4aa153 |
completed | April 1, 2026, 12:16 a.m. |
Created at: March 30, 2026, 5:27 p.m.