Triple
T4369052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Toll of the Sea |
E98848
|
entity |
| Predicate | hasAnnaMayWongRoleType |
P14496
|
FINISHED |
| Object | first leading role for Anna May Wong |
—
|
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: first leading role for Anna May Wong | Statement: [The Toll of the Sea, hasAnnaMayWongRoleType, first leading role for Anna May Wong]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAnnaMayWongRoleType Context triple: [The Toll of the Sea, hasAnnaMayWongRoleType, first leading role for Anna May Wong]
-
A.
actingRoleType
Indicates the specific type or category of role an entity performs when acting in a particular capacity or function.
-
B.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
C.
hasMainRole
chosen
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
-
D.
hasFamousNamesakeRole
Indicates that an entity has a role or position that shares its name with a well-known or historically notable person.
-
E.
hasCrewRole
Indicates that an entity serves in a specific role or position within a crew associated with 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352052b388190b02cca9a3b480be4 |
completed | March 12, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69b34f53e3cc8190bf5d4dbe2413bf65 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:17 p.m.