Triple
T11334761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Fields |
E268441
|
entity |
| Predicate | startTimeOfRole_Dorothy "Tootie" Ramsey |
P44516
|
FINISHED |
| Object | 1979 |
—
|
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: 1979 | Statement: [Kim Fields, startTimeOfRole_Dorothy "Tootie" Ramsey, 1979]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startTimeOfRole_Dorothy "Tootie" Ramsey Context triple: [Kim Fields, startTimeOfRole_Dorothy "Tootie" Ramsey, 1979]
-
A.
danceRole
Indicates that one entity has a specific role or function within a dance performed with or in relation to another entity.
-
B.
talentShowRole
Indicates the specific function or position an entity holds within the context of a talent show (e.g., performer, judge, host).
-
C.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
D.
theaterRole
chosen
Indicates that an entity holds or performs a specific role or character in a theatrical production in relation to another entity (such as a play or performance).
-
E.
MarilynMonroeRoleType
Indicates the type or category of role associated with Marilyn Monroe in a given context.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9ff62c88190a089dd84e2ac1d17 |
completed | April 9, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69d787afe5a48190b8af1a3e19529641 |
completed | April 9, 2026, 11:04 a.m. |
Created at: April 8, 2026, 9:33 p.m.