Triple
T15232055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dame J |
E364027
|
entity |
| Predicate | indicatesOffice |
P66768
|
FINISHED |
| Object | judicial office |
—
|
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: judicial office | Statement: [Dame J, indicatesOffice, judicial office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: indicatesOffice Context triple: [Dame J, indicatesOffice, judicial office]
-
A.
specifiesOffice
chosen
Indicates that an entity is assigned to or associated with a particular office or official position.
-
B.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
-
C.
includedOffice
Indicates that one office is contained within, or forms part of, another office or organizational unit.
-
D.
representsInOffice
Indicates that one entity serves as an official representative of another entity within a specific office, position, or institutional role.
-
E.
officeIsIn
Indicates that one office is located within or inside another specified place or building.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078e27408190bc13c0ca441f5594 |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca899d5c8190be4a7c71e1683c69 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:12 a.m.