Triple
T13080008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maggie Cassidy |
E310175
|
entity |
| Predicate | mainCharacterHobby |
P8834
|
FINISHED |
| Object | football |
—
|
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: football | Statement: [Maggie Cassidy, mainCharacterHobby, football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCharacterHobby Context triple: [Maggie Cassidy, mainCharacterHobby, football]
-
A.
hasNotableHobby
Indicates that an entity engages in a hobby that is distinctive, remarkable, or otherwise noteworthy.
-
B.
typicalActivity
Indicates that an entity is commonly or characteristically engaged in a particular activity.
-
C.
isPopularWithHobbyKeepers
Indicates that something is widely liked, favored, or well-regarded by people who pursue a particular hobby or collection activity.
-
D.
activityType
chosen
Indicates the specific kind or category of action or event that an entity is engaged in or associated with.
-
E.
semiAutobiographicalCharacter
Indicates that a character is based partly on the real-life experiences, personality, or identity of its creator or author, but is not a fully direct self-portrayal.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d98119cb7081908b78ffe83ec99851 |
completed | April 10, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69d9803d46688190bac6b7d208f08d01 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:01 p.m.