Triple
T29894990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Happiness Is a Four-letter Word |
E759254
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Khanyi Mbau
Khanyi Mbau is a South African actress, television personality, and media figure known for her prominent roles in film and TV as well as her high-profile public persona.
|
E1893412
|
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: Khanyi Mbau | Statement: [Happiness Is a Four-letter Word, castMember, Khanyi Mbau]
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: Khanyi Mbau Triple: [Happiness Is a Four-letter Word, castMember, Khanyi Mbau]
Generated description
Khanyi Mbau is a South African actress, television personality, and media figure known for her prominent roles in film and TV as well as her high-profile public persona.
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_69f2245f1cf88190978c70d1a1d2cb73 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6772a62a08190a8f625b73e261ba9 |
completed | May 2, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2721e3d6588190bffbeb1a9cf79f40 |
completed | June 8, 2026, 8:11 p.m. |
| NEDg | Description generation | batch_6a27226c29fc81909e79cb508975bc92 |
completed | June 8, 2026, 8:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a27231dfdac8190870ab5105b8c174d |
completed | June 8, 2026, 8:16 p.m. |
Created at: April 29, 2026, 6:04 p.m.