Triple
T4521613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suit & Tie |
E103281
|
entity |
| Predicate | featuresSpokenIntro |
P56242
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Suit & Tie, featuresSpokenIntro, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresSpokenIntro Context triple: [Suit & Tie, featuresSpokenIntro, true]
-
A.
speakerConfiguration
Indicates how speakers are arranged or assigned within an audio or communication setup.
-
B.
speakerFeatures
Indicates that certain characteristics, attributes, or properties are associated with a speaker in a given context.
-
C.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
D.
hasSpokenWordIntro
chosen
Indicates that an entity includes or features an introductory segment performed in spoken word form.
-
E.
featuresSuit
Indicates that one entity includes or presents a particular suit (e.g., clothing, armor, or outfit) as a notable component or attribute.
- 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_69bd43dba59881908cf59b31df8c7ae1 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd574bd6908190b939d92b5809b101 |
completed | March 20, 2026, 2:18 p.m. |
| PD | Predicate disambiguation | batch_69bd521cf77c819083852de3094d1377 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:02 p.m.