Triple
T25148754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TED Talk "Designing books is no laughing matter. OK, it is." |
E630011
|
entity |
| Predicate | speakerProfession |
P72070
|
FINISHED |
| Object | book cover designer |
—
|
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: book cover designer | Statement: [TED Talk "Designing books is no laughing matter. OK, it is.", speakerProfession, book cover designer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: speakerProfession Context triple: [TED Talk "Designing books is no laughing matter. OK, it is.", speakerProfession, book cover designer]
-
A.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
B.
primarySpeakersOccupation
Indicates the main or most common occupation held by the speakers of a given language.
-
C.
titleHolderProfession
Indicates that the profession or occupation of the entity holding a particular title is the specified value.
-
D.
presenterOccupation
chosen
Indicates that an entity serves in a specific professional role or job as a presenter.
-
E.
authorOccupation
Indicates the professional role or job that an author holds or is associated with.
- 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_69e2ff349e408190a6f4a5a66279f54d |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f4684f11708190aa73600e3367475b |
completed | May 1, 2026, 8:46 a.m. |
| PD | Predicate disambiguation | batch_69f44d8043b081908bbffd7f044b4f26 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 6:30 a.m.