Triple
T13102786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matilda |
E310759
|
entity |
| Predicate | hasTelevisionRating |
P72300
|
FINISHED |
| Object | PG |
—
|
LITERAL 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: PG | Statement: [Matilda, hasTelevisionRating, PG]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTelevisionRating Context triple: [Matilda, hasTelevisionRating, PG]
-
A.
televisionRating
chosen
Indicates the content rating or audience suitability classification assigned to a television program.
-
B.
TVRating
Indicates the content rating assigned to a television program, reflecting its suitability for specific audiences.
-
C.
mpaaRating
Indicates the official Motion Picture Association of America (MPAA) content rating assigned to a film or audiovisual work.
-
D.
USRating
Indicates that an entity has been assigned a rating, classification, or evaluation according to a United States–based standard or system.
-
E.
hasContentRating
Indicates that something is associated with a specified content rating that reflects its suitability for particular audiences.
- F. None of above.
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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98153255c8190b6ab64ac0c4716f8 |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d98041a3548190a05ddd83dbb660fa |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:04 p.m.