Triple
T37129004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puppy Co. |
E919466
|
entity |
| Predicate | threatensInFiction |
P122093
|
FINISHED |
| Object | baby cuteness market share |
—
|
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: baby cuteness market share | Statement: [Puppy Co., threatensInFiction, baby cuteness market share]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: threatensInFiction Context triple: [Puppy Co., threatensInFiction, baby cuteness market share]
-
A.
threatenedByInFiction
Indicates that, within a fictional context, one entity is portrayed as being threatened by another entity.
-
B.
threatInStory
chosen
Indicates that one entity poses or represents a danger, menace, or harmful intent toward another entity within the context of a narrative or story.
-
C.
safetyInFiction
Indicates that a work of fiction portrays conditions, measures, or themes related to safety, risk, or protection within its narrative world.
-
D.
hasRiskInFiction
Indicates that a subject is associated with a potential danger, threat, or harmful outcome within a fictional or narrative context.
-
E.
threatType
Indicates the specific category or nature of a threat that one entity poses or represents in relation to another.
- 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_69f76e9d13e48190a108f7fbf80ff375 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fbaebc8f2c8190b94f1b4a3ec92e8c |
completed | May 6, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69fbadf1e6008190a71bbd196ba06844 |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:15 p.m.