Triple
T3122044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sylvia Robinson |
E65205
|
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 Robinson, givenName, Sylvia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sylvia Context triple: [Sylvia Robinson, givenName, Sylvia]
-
A.
Sylvia
chosen
Sylvia is a feminine given name of Latin origin meaning "from the forest" or "of the woods."
-
B.
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.
-
C.
Suzanne
"Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
-
D.
Suzanne
Suzanne is a central character in Steve Martin’s play "Picasso at the Lapin Agile," representing a young woman entangled romantically with both Picasso and other men in the bohemian Parisian setting.
-
E.
Lark
Lark was a famous overnight passenger train that ran between San Francisco and Los Angeles, known for its streamlined design and sleeper service.
- 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_69ad8580c72481909672d37acf647893 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada52ab05c819088204bbb608c837c |
completed | March 8, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f6f57fc8190a5f50028792ac394 |
completed | March 12, 2026, 12:57 a.m. |
Created at: March 8, 2026, 3:04 p.m.