Triple
T19956748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kirk Michael |
E479702
|
entity |
| Predicate | onTTSection |
P3120
|
FINISHED |
| Object | Kirk Michael to Bishopscourt section |
—
|
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: Kirk Michael to Bishopscourt section | Statement: [Kirk Michael, onTTSection, Kirk Michael to Bishopscourt section]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: onTTSection Context triple: [Kirk Michael, onTTSection, Kirk Michael to Bishopscourt section]
-
A.
section
chosen
Indicates that one entity is a distinct part, division, or segment of another entity within a larger whole.
-
B.
sectionType
Indicates the specific kind or category of section that an entity belongs to or represents.
-
C.
hasSectionOn
Indicates that one entity (typically a document or resource) contains a dedicated section or part that specifically addresses or discusses another entity or topic.
-
D.
typicalSection
Indicates that one section is a standard, representative, or commonly occurring instance within a broader set or structure of sections.
-
E.
sectionProvided
Indicates that a specific section or portion of content has been supplied or made available by one entity to another.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65af08e008190a3a1b807b638a99e |
completed | April 20, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_69e537f7e4848190b431a69ec3f1b609 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:54 p.m.