Triple
T23288769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgia Young |
E589966
|
entity |
| Predicate | hasCloseFriend |
P49697
|
FINISHED |
| Object | Violet |
—
|
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: Violet | Statement: [Georgia Young, hasCloseFriend, Violet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Violet Context triple: [Georgia Young, hasCloseFriend, Violet]
-
A.
Violet
Violet is a live-action short film recognized with the Academy Award for Best Live Action Short Film at the 54th Oscars.
-
B.
Violet
Violet is the given first name of the English actress Anne Heywood, known for her film and television roles in the mid-20th century.
-
C.
Violet
Violet is the given name of Helen Violet Asquith, a British aristocrat and social figure of the early 20th century.
-
D.
Violet
Violet is the former nickname associated with New York University’s athletic teams, historically reflecting the school’s signature color.
-
E.
Violet
Violet is a feminine given name often associated with the purple flower and the color violet, conveying themes of beauty and delicacy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69e25d1af9d88190a0b9b5e8fa608618 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19649570c8190b565fafa55b1f886 |
completed | April 29, 2026, 5:25 a.m. |
Created at: April 17, 2026, 5:01 p.m.