Triple
T1801484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Man of the Moment |
E39727
|
entity |
| Predicate | hasLeadActorRoleType |
P16411
|
FINISHED |
| Object | hapless clerk turned diplomat |
—
|
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: hapless clerk turned diplomat | Statement: [Man of the Moment, hasLeadActorRoleType, hapless clerk turned diplomat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLeadActorRoleType Context triple: [Man of the Moment, hasLeadActorRoleType, hapless clerk turned diplomat]
-
A.
hasMainRole
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
-
B.
actingRoleType
chosen
Indicates the specific type or category of role an entity performs when acting in a particular capacity or function.
-
C.
hasCrewRole
Indicates that an entity serves in a specific role or position within a crew associated with another entity.
-
D.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
E.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
- 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_69a88632aa588190ba3978fde0db5bbd |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba67721788190951beae25e885457 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61d514c081908197ac1f7c7d7a88 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.