Triple
T13036368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Baxter |
E326570
|
entity |
| Predicate | jobTitleInSeries |
P38167
|
FINISHED |
| Object | anchorman at WJM-TV |
—
|
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: anchorman at WJM-TV | Statement: [Ted Baxter, jobTitleInSeries, anchorman at WJM-TV]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jobTitleInSeries Context triple: [Ted Baxter, jobTitleInSeries, anchorman at WJM-TV]
-
A.
seriesTitleOfRole
Indicates that a given title is the name of the series associated with a particular role.
-
B.
narrativeRoleInSeries
Indicates the specific narrative function or role an entity plays within a particular series or serialized work.
-
C.
starOccupationInSeries
Indicates that an individual has a specific occupation or role as a starring character within a particular series.
-
D.
spouseOccupationInSeries
Indicates that a character’s spouse has a particular occupation within the context of a series.
-
E.
titleOrRole
chosen
Indicates that one entity serves as the title, position, or role held or described by 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:55 p.m.