Triple
T20294469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marilyn Monroe: An Appreciation |
E510110
|
entity |
| Predicate | hasNarrativeComponent |
P6847
|
FINISHED |
| Object | commentary by Eve Arnold |
—
|
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: commentary by Eve Arnold | Statement: [Marilyn Monroe: An Appreciation, hasNarrativeComponent, commentary by Eve Arnold]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNarrativeComponent Context triple: [Marilyn Monroe: An Appreciation, hasNarrativeComponent, commentary by Eve Arnold]
-
A.
hasNarrative
Indicates that one entity contains, presents, or is associated with a story or narrative about another entity or subject.
-
B.
hasPartInNarrative
Indicates that one entity plays a role or participates as a component within the storyline or structure of another narrative entity.
-
C.
hasNarrativeSegments
Indicates that an entity is composed of or associated with multiple distinct narrative segments or sections.
-
D.
containsNarrativeOf
chosen
Indicates that one entity includes or presents the story, account, or narrative content of another entity.
-
E.
hasNarrativeOutcome
Indicates that an event, action, or narrative element leads to or results in a particular story-related consequence or resolution.
- 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_69e0b4c652388190b782cad965e5a098 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e67704262c8190bc903b733d849881 |
completed | April 20, 2026, 6:57 p.m. |
| PD | Predicate disambiguation | batch_69e55b21b09081909e46691b6f45a07f |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 16, 2026, 11:13 a.m.