Triple
T29891709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Remo |
E759167
|
entity |
| Predicate | marketingHighlight |
P111669
|
FINISHED |
| Object | Sivakarthikeyan nurse getup |
—
|
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: Sivakarthikeyan nurse getup | Statement: [Remo, marketingHighlight, Sivakarthikeyan nurse getup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marketingHighlight Context triple: [Remo, marketingHighlight, Sivakarthikeyan nurse getup]
-
A.
marketingEmphasized
Indicates that a marketing effort or message is given special focus, prominence, or priority relative to other aspects or activities.
-
B.
marketingFeature
chosen
Indicates that something is being promoted or highlighted as a selling point or advantage for marketing purposes.
-
C.
marketingNameEmphasizes
Indicates that a marketing name highlights or draws special attention to a particular feature, attribute, or aspect of something.
-
D.
highlightsSign
Indicates that one entity visually emphasizes or draws special attention to a sign.
-
E.
highlights
Indicates that one entity draws special attention to, emphasizes, or visually marks another entity as important or noteworthy.
- 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_69f2245f1cf88190978c70d1a1d2cb73 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6b2a65c7c8190ac40f1466ceadefc |
completed | May 3, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f6b14d7d508190bc7d4c89dfba4a32 |
completed | May 3, 2026, 2:22 a.m. |
Created at: April 29, 2026, 6:02 p.m.