Triple
T28209107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Karenina (1935 film) |
E717107
|
entity |
| Predicate | starsHollywoodIcon |
P5563
|
FINISHED |
| Object | Greta Garbo |
—
|
NE NERFINISHED |
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: Greta Garbo | Statement: [Anna Karenina (1935 film), starsHollywoodIcon, Greta Garbo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: starsHollywoodIcon Context triple: [Anna Karenina (1935 film), starsHollywoodIcon, Greta Garbo]
-
A.
stars
Indicates that one entity marks, highlights, or designates another as special, important, or featured (often by assigning a star or similar marker).
-
B.
starredActor
chosen
Indicates that an actor performed a leading or significant role in a particular production or work.
-
C.
notableStar
Indicates that the subject is a star (or stellar object) that is distinguished or noteworthy in some significant way, such as brightness, fame, or scientific interest, relative to other stars.
-
D.
starOccupation
Indicates that an entity is the primary or featured performer in a particular occupation, role, or professional capacity.
-
E.
madeInHollywood
Indicates that something was produced or created within Hollywood, typically referring to the Hollywood film or entertainment industry.
- 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_69efd6b826908190857e6e7dad74ed93 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f64310b67481908c2de1bc652d4d87 |
completed | May 2, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69f63c6c1a948190b68c0f92c264cc0c |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 10:37 p.m.