Triple
T25148755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TED Talk "Designing books is no laughing matter. OK, it is." |
E630011
|
entity |
| Predicate | speakerName |
P90848
|
FINISHED |
| Object | Chip Kidd |
—
|
NE NERFINISHED |
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: Chip Kidd | Statement: [TED Talk "Designing books is no laughing matter. OK, it is.", speakerName, Chip Kidd]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: speakerName Context triple: [TED Talk "Designing books is no laughing matter. OK, it is.", speakerName, Chip Kidd]
-
A.
speakerMP
Indicates that the subject is a Member of Parliament who holds or is identified with the role of Speaker (or a speaker-related parliamentary position).
-
B.
speakerInText
chosen
Indicates that a given entity is the person who speaks or is quoted within a particular text or textual segment.
-
C.
speechAlsoKnownAs
Indicates that a speech or spoken work is referred to by an alternative name, title, or alias.
-
D.
speakerType
Indicates the role or category of a participant in a communicative act (e.g., narrator, quoted speaker, system voice) within a given context.
-
E.
identifiesSpeakerAs
Indicates that one entity designates or recognizes another entity as the speaker of a given utterance or communication.
- 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_69f45cfb53f4819099bba48c5057e787 |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 18, 2026, 6:30 a.m.