Triple
T11541510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mad Dash Racing |
E273685
|
entity |
| Predicate | hasESRBRating |
P39176
|
FINISHED |
| Object | E for Everyone |
E367838
|
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: E for Everyone | Statement: [Mad Dash Racing, hasESRBRating, E for Everyone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: E for Everyone Context triple: [Mad Dash Racing, hasESRBRating, E for Everyone]
-
A.
E for Everyone
chosen
E for Everyone is an ESRB content rating indicating that a video game is generally suitable for players of all ages.
-
B.
ESRB E
ESRB E is the Entertainment Software Rating Board’s “Everyone” rating, indicating that a video game is generally suitable for all ages.
-
C.
EW
EW is the common abbreviation for Entertainment Weekly, a popular American magazine and website covering movies, television, music, books, and pop culture.
-
D.
EW
EW is the stock ticker symbol for Edwards Lifesciences, a medical technology company best known for its heart valves and critical care monitoring products.
-
E.
E
E is a rapid transit line of the Buenos Aires Underground system in Argentina.
- 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_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d886e09eec8190894069d86b79183d |
completed | April 10, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e685ab77908190ac5d59cf2b8c96bf |
completed | April 20, 2026, 7:59 p.m. |
Created at: April 8, 2026, 9:37 p.m.