Triple
T23086167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caroline Lavinia Scott |
E575606
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Caroline |
—
|
NE NERFINISHED |
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: Caroline | Statement: [Caroline Lavinia Scott, givenName, Caroline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caroline Context triple: [Caroline Lavinia Scott, givenName, Caroline]
-
A.
Caroline
Caroline is a rural town in Tompkins County, New York, known for its small communities, scenic landscapes, and proximity to the city of Ithaca.
-
B.
Caroline
Caroline was a British princess of the early 18th century, the daughter of King George II and Queen Caroline of Ansbach.
-
C.
Caroline
chosen
Caroline is a feminine given name of French and Latin origin, commonly used in English-speaking and European countries.
-
D.
Caroline
Caroline is a supporting character in the romantic comedy film "Chalet Girl," involved in the story’s ski-resort social circle and romantic entanglements.
-
E.
Caroline
Caroline is a Danish princess, known formally as Princess Wilhelmina Caroline of Denmark, who lived in the 18th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245bf3e3c819086d3448720efc01b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18da573588190a8e0c859667e64a6 |
completed | April 29, 2026, 4:48 a.m. |
Created at: April 17, 2026, 3:57 p.m.