Triple
T21250830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothy Michaels |
E523738
|
entity |
| Predicate | becomesPopularWith |
P729
|
FINISHED |
| Object | TV audience in the film |
—
|
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: TV audience in the film | Statement: [Dorothy Michaels, becomesPopularWith, TV audience in the film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: becomesPopularWith Context triple: [Dorothy Michaels, becomesPopularWith, TV audience in the film]
-
A.
isPopularWith
chosen
Indicates that one entity is well-liked, favored, or widely accepted by another entity or group.
-
B.
isPopularAs
Indicates that an entity is widely liked, well-known, or favored in a particular role, context, or capacity.
-
C.
gainedProminenceFor
Indicates that an entity became widely recognized or notable specifically because of another entity, action, or achievement.
-
D.
popularizedBy
Indicates that something became widely known, accepted, or fashionable as a result of the influence or actions of a particular agent.
-
E.
gainedProminenceAs
Indicates that an entity became widely recognized or notable in the role, capacity, or identity specified by another entity.
- 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_69e0b5146c108190adc9adb73e90abff |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7359da6e08190a96c471463c2388d |
completed | April 21, 2026, 8:30 a.m. |
| PD | Predicate disambiguation | batch_69e5f61239708190ab7b3c83ae848a0d |
completed | April 20, 2026, 9:46 a.m. |
Created at: April 16, 2026, 3:56 p.m.