Triple
T28701692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Play-Tone Records |
E729567
|
entity |
| Predicate | hasFictionalBranding |
P11989
|
FINISHED |
| Object | Play-Tone logo |
—
|
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: Play-Tone logo | Statement: [Play-Tone Records, hasFictionalBranding, Play-Tone logo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalBranding Context triple: [Play-Tone Records, hasFictionalBranding, Play-Tone logo]
-
A.
hasFictionalProduct
Indicates a relationship where one entity features, offers, or includes a product that exists only in fiction or an imagined context.
-
B.
hasFictionalSignText
Indicates that an entity bears or is associated with text appearing on a fictional sign (e.g., wording depicted on an in-universe sign).
-
C.
hasFictionalSponsor
Indicates that an entity is sponsored or endorsed by a sponsor that is fictional rather than real.
-
D.
hasFictionalCorporation
Indicates that an entity is associated with or includes a fictional corporation within its content, setting, or narrative.
-
E.
hasBranding
chosen
Indicates that one entity carries, displays, or is associated with the brand identity of 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_69f043e6e9688190b6bdd6e5665498ff |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69fd4f39b5008190b83b3227ce22c509 |
completed | May 8, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69fd4df17c548190a4e2a6fea70f7e10 |
completed | May 8, 2026, 2:44 a.m. |
Created at: April 28, 2026, 5:42 a.m.