Triple
T32490243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blacc Hollywood |
E830363
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
You and Your Friends
"You and Your Friends" is a party-themed hip hop track by Wiz Khalifa from his album *Blacc Hollywood*, known for its club-ready production and catchy hook.
|
E2008120
|
NE 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: You and Your Friends | Statement: [Blacc Hollywood, hasPart, You and Your Friends]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: You and Your Friends Triple: [Blacc Hollywood, hasPart, You and Your Friends]
Generated description
"You and Your Friends" is a party-themed hip hop track by Wiz Khalifa from his album *Blacc Hollywood*, known for its club-ready production and catchy hook.
Provenance (5 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_69f34920aa4081908d8fb0277414b911 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c4070c808190b633acf2a95b4e56 |
completed | May 3, 2026, 3:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3466a7ef648190843fdc8877d9c3a1 |
completed | June 18, 2026, 9:44 p.m. |
| NEDg | Description generation | batch_6a3468112b0c819084fff468a94420ad |
completed | June 18, 2026, 9:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3468d7b1e08190ba5fa17f9e3547aa |
completed | June 18, 2026, 9:53 p.m. |
Created at: May 1, 2026, 12:59 a.m.