Triple
T37362127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ronald Maurice Darling Jr. |
E927609
|
entity |
| Predicate | televisionAnalystFor |
P188310
|
FINISHED |
| Object | SNY |
—
|
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: SNY | Statement: [Ronald Maurice Darling Jr., televisionAnalystFor, SNY]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: televisionAnalystFor Context triple: [Ronald Maurice Darling Jr., televisionAnalystFor, SNY]
-
A.
televisionCategory
Indicates the classification or genre category assigned to a television-related entity (such as a show, channel, or program).
-
B.
televisionActivity
Indicates engaging in activities related to watching, using, or interacting with a television.
-
C.
televisionPlatform
Indicates that one entity serves as the television platform (e.g., service, system, or distribution channel) through which the other entity’s TV content is delivered or accessed.
-
D.
televisionShare
Indicates the proportion of total television audience or viewing time captured by a particular program, channel, or entity relative to all viewing in a given market or period.
-
E.
televisionWork
Indicates a relationship where a creative work is produced for, broadcast on, or primarily associated with television.
- F. None of above. chosen
Provenance (4 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_69f76eb701788190b40824bc4594d985 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba68077788190b311e027435fcf87 |
completed | May 6, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fba34c65ac8190b298f0f00d1dcc0e |
completed | May 6, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69fba67f78348190ab160988e4698394 |
completed | May 6, 2026, 8:37 p.m. |
Created at: May 3, 2026, 4:16 p.m.