Triple
T1240415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chief Justice |
E26644
|
entity |
| Predicate | isOftenStyle |
P19247
|
FINISHED |
| Object | Chief Justice of [Country] |
—
|
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: Chief Justice of [Country] | Statement: [Chief Justice, isOftenStyle, Chief Justice of [Country]]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isOftenStyle Context triple: [Chief Justice, isOftenStyle, Chief Justice of [Country]]
-
A.
styleTendsTo
chosen
Indicates that one style is generally inclined or likely to develop, appear, or be adopted in the direction of another style.
-
B.
usedWithStyle
Indicates that something is employed or applied in conjunction with a particular style or stylistic manner.
-
C.
isMostly
Indicates that one entity constitutes the greater part or majority of another entity in amount, extent, or composition.
-
D.
isFrequentlyAdapted
Indicates that a work or source material is often transformed or re-created into new formats or versions, such as films, plays, or other media.
-
E.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf4343e48190a232abd8475880a0 |
completed | March 1, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69a4bb696a38819095845c84f0241287 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.