Triple
T2643063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mistress Quickly |
E62918
|
entity |
| Predicate | speechCharacteristic |
P40912
|
FINISHED |
| Object | frequent misuse of words |
—
|
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: frequent misuse of words | Statement: [Mistress Quickly, speechCharacteristic, frequent misuse of words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: speechCharacteristic Context triple: [Mistress Quickly, speechCharacteristic, frequent misuse of words]
-
A.
speakerFeatures
chosen
Indicates that certain characteristics, attributes, or properties are associated with a speaker in a given context.
-
B.
vocalizationCharacteristic
Indicates how an entity’s vocal sounds are characterized, such as their quality, style, or distinctive acoustic features.
-
C.
speechType
Indicates the specific category or form of spoken or written communication that an utterance or speech act belongs to (e.g., question, statement, command).
-
D.
speechContent
Indicates that one entity expresses, conveys, or contains the spoken or written content associated with another entity’s act of speaking or communication.
-
E.
voiceType
Indicates the specific vocal style, quality, or role associated with an entity’s voice in a given context.
- 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_69ab4c3f2dcc819082df80f5e032f690 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abd8ff34988190ba9d69ce9d77c71d |
completed | March 7, 2026, 7:51 a.m. |
| PD | Predicate disambiguation | batch_69abd814298c8190952f05aed43f6bb8 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:53 p.m.