Triple
T24544727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mona Lisa (1986 film) |
E607188
|
entity |
| Predicate | leadActorForCharacterSimone |
P94795
|
FINISHED |
| Object | Cathy Tyson |
—
|
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: Cathy Tyson | Statement: [Mona Lisa (1986 film), leadActorForCharacterSimone, Cathy Tyson]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadActorForCharacterSimone Context triple: [Mona Lisa (1986 film), leadActorForCharacterSimone, Cathy Tyson]
-
A.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
leadActorForCharacterLance
Indicates that the referenced person is the primary actor who portrays the character named Lance.
-
C.
leadActorForCharacterJade
Indicates that the subject is the primary actor who portrays the character named Jade.
-
D.
playedBy
Indicates that a role, character, or performance is portrayed or executed by a specific person or agent.
-
E.
performerCharacterName
chosen
Indicates that a performer is associated with or portrays a specific character name in a performance or work.
- 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_69e2c4c9bf94819082d05da6f5c29907 |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2be044d4c819094e14eda28d371a7 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6b0ca8081908d931aec560eae56 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:26 a.m.