Triple
T6437967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Personal Velocity |
E129945
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Greta |
E114897
|
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: Greta | Statement: [Personal Velocity, hasMainCharacter, Greta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greta Context triple: [Personal Velocity, hasMainCharacter, Greta]
-
A.
Greta
Greta is a small town located within the Hunter Region of New South Wales, Australia.
-
B.
Greta
chosen
Greta is a feminine given name, commonly used as a diminutive or variant of names like Margaret in various European languages.
-
C.
Ottilia
Ottilia is a feminine given name of Germanic origin, related to Otto and typically interpreted to mean "wealth" or "prosperity."
-
D.
Katrin
Katrin is a feminine given name, commonly used in various European countries, that is a variant of the name Catherine.
-
E.
Greta Lovisa Gustafsson
Greta Lovisa Gustafsson, better known as Greta Garbo, was a legendary Swedish-American film actress renowned for her enigmatic screen presence and iconic roles during Hollywood’s silent and early sound eras.
- 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_69c0084caac48190a7bc2ad8ba44536f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c06964186c8190aeeb0038f4696032 |
completed | March 22, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c640f56ee881909f7b7f0909e1d701 |
completed | March 27, 2026, 8:33 a.m. |
Created at: March 22, 2026, 4:45 p.m.