Triple
T5364096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sylvia Townsend Warner |
E103088
|
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 Townsend Warner, givenName, Sylvia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sylvia Context triple: [Sylvia Townsend Warner, 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’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.
-
D.
Sylvia’s Mother
"Sylvia’s Mother" is a country-pop song, popularized by Dr. Hook & the Medicine Show, that tells a bittersweet story of lost love through a one-sided phone call.
-
E.
Lila
Lila is a central female character in Max Frisch’s novel "Mein Name sei Gantenbein," around whom the narrator constructs one of his imagined lives and relationships.
- 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_69bd43daa3e4819090b59d127db70e57 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd865d42508190a1a96121674c1020 |
completed | March 20, 2026, 5:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21f2bc708190b596c5402fa07e75 |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:02 p.m.