Triple
T29947327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seema Raja |
E760669
|
entity |
| Predicate | featuresCommercialEntertainmentElements |
P120897
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Seema Raja, featuresCommercialEntertainmentElements, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCommercialEntertainmentElements Context triple: [Seema Raja, featuresCommercialEntertainmentElements, true]
-
A.
featuredElement
Indicates that one element is highlighted or given special prominence relative to others in a given context.
-
B.
entertainmentFocus
chosen
Indicates that one entity is primarily concerned with, directed toward, or centered on providing or engaging in entertainment for another entity or context.
-
C.
featuredMove
Indicates that a particular move is highlighted or given special prominence among other moves.
-
D.
featuredFor
Indicates that one entity is highlighted, promoted, or specially showcased in the context or for the benefit of another entity.
-
E.
specialFeatures
Indicates the distinctive or additional characteristics, functionalities, or attributes that set an entity apart from standard or typical versions.
- 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_69f2246562b881909d57622f4086d43d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69ffacdf9f5c8190baef0245edfe87fc |
completed | May 9, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69ffac5e86e08190a1e6da0840a237ad |
completed | May 9, 2026, 9:51 p.m. |
Created at: April 29, 2026, 6:24 p.m.