Triple
T21708565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Sara |
E535836
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Legend |
—
|
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: Legend | Statement: [Mia Sara, notableWork, Legend]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Legend Context triple: [Mia Sara, notableWork, Legend]
-
A.
Legend
chosen
Legend is a 1985 dark fantasy film directed by Ridley Scott, known for its lush visual style, fairy-tale atmosphere, and Tim Curry’s iconic portrayal of the Lord of Darkness.
-
B.
Legend
Legend was the original brand name of the Chinese technology company now known globally as Lenovo.
-
C.
Legend
Legend is a greatest hits compilation album by Bob Marley and the Wailers that has become one of the best-selling and most influential reggae records of all time.
-
D.
Legend
Legend is the official mascot character created for the 2015 World Aquatics Championships held in Kazan, Russia.
-
E.
Legend
Legend is a musical artist known for creating tracks like "Dope" within contemporary hip-hop and rap scenes.
- 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_69e0c46b44c0819088ab883ebd44e0e8 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efb5314a288190b4b8347cca15aaa8 |
completed | April 27, 2026, 7:12 p.m. |
Created at: April 16, 2026, 6:46 p.m.