Triple
T28179124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | College Hill |
E715976
|
entity |
| Predicate | networkGenreFocus |
P167908
|
FINISHED |
| Object | African-American audiences |
—
|
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: African-American audiences | Statement: [College Hill, networkGenreFocus, African-American audiences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: networkGenreFocus Context triple: [College Hill, networkGenreFocus, African-American audiences]
-
A.
targetGenre
Indicates the genre that something is specifically aimed at, categorized under, or intended to belong to.
-
B.
genreWithin
Indicates that one genre is a subgenre or more specific category contained within another, broader genre.
-
C.
websiteGenre
Indicates the thematic category or type of content that a website is primarily associated with.
-
D.
fandomGenre
Indicates that something belongs to, is associated with, or is categorized under a particular fandom-related genre.
-
E.
commonGenre
Indicates that two entities share at least one genre in common.
- 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_69efd6b4fc5c81909dd88f01a8c2b35d |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f66e5f7e30819094530abceabd5f43 |
completed | May 2, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69f66abfdaf08190a55f14c70be6fd4d |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66d75a8788190aa9ca2c977429045 |
completed | May 2, 2026, 9:32 p.m. |
Created at: April 27, 2026, 10:18 p.m.