Triple
T2436649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mervyn LeRoy |
E52975
|
entity |
| Predicate | wasInfluentialIn |
P24496
|
FINISHED |
| Object | classic Hollywood cinema |
—
|
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: classic Hollywood cinema | Statement: [Mervyn LeRoy, wasInfluentialIn, classic Hollywood cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasInfluentialIn Context triple: [Mervyn LeRoy, wasInfluentialIn, classic Hollywood cinema]
-
A.
hasHistoricalInfluenceFrom
Indicates that one entity’s characteristics, development, or significance have been shaped or affected by the past actions, ideas, or legacy of another entity.
-
B.
hasHistoricalWritingInfluenceFrom
Indicates that one entity’s historical writing style, content, or traditions are influenced by those of another entity.
-
C.
wasProminentIn
chosen
Indicates that an entity was notably active, influential, or widely recognized within a particular field, context, or time period.
-
D.
placeOfInfluence
Indicates the location or area where an entity exerts significant impact, authority, or cultural, social, or intellectual influence.
-
E.
influencedNameOf
Indicates that one entity has affected or shaped the naming or choice of name of 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_69ab4959bcc0819083246f9fb10439e3 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcebf7cac8190889e6890d72c256c |
completed | March 7, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69abc5ac11b081908ce6a506e81a742a |
completed | March 7, 2026, 6:29 a.m. |
Created at: March 6, 2026, 9:43 p.m.