Triple
T480718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nollywood |
E9159
|
entity |
| Predicate | comparison |
P278
|
FINISHED |
| Object | often compared to Hollywood |
—
|
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: often compared to Hollywood | Statement: [Nollywood, comparison, often compared to Hollywood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: comparison Context triple: [Nollywood, comparison, often compared to Hollywood]
-
A.
isComparedTo
chosen
Indicates that one entity is evaluated or measured in relation to another to highlight similarities, differences, or relative qualities.
-
B.
competition
Indicates a relationship where two or more entities strive against each other to achieve a superior outcome, advantage, or reward.
-
C.
oftenContrastedWith
Indicates that one entity is frequently compared to another in a way that highlights their differences or opposing characteristics.
-
D.
distinction
Indicates that one entity is recognized, treated, or classified as different or separate from another.
-
E.
parity
Indicates that two quantities share the same evenness or oddness, or more generally that they have equivalent status or value 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f058ebe48190aaa0a829b21f75fa |
completed | Feb. 28, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69a2edf321288190b5d560f75782c2cb |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.