Triple
T32418509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zebra Katz |
E828383
|
entity |
| Predicate | performsSubjectMatter |
P195940
|
FINISHED |
| Object | gender |
—
|
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: gender | Statement: [Zebra Katz, performsSubjectMatter, gender]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: performsSubjectMatter Context triple: [Zebra Katz, performsSubjectMatter, gender]
-
A.
subjectMatter
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
B.
subjectMatterScope
Indicates the thematic or topical domain that an action, statement, or resource pertains to or falls within.
-
C.
treatmentOfSubjects
Indicates that an entity administers or applies a treatment, intervention, or procedure to specific subjects.
-
D.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
-
E.
basedOnSubjectOf
Indicates that something is derived from, informed by, or constructed using the subject matter or content of another entity.
- F. None of above. chosen
Provenance (4 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_69f34919f300819092b541c6277cd68a |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fdf5d05cc481909ec9e1b1f0784279 |
completed | May 8, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69fdf0cdd6948190838864ab3120dfa6 |
completed | May 8, 2026, 2:18 p.m. |
| PDg | Predicate description generation | batch_69fdf5cfa1ec8190b80d887fa1bfb4cf |
completed | May 8, 2026, 2:40 p.m. |
Created at: May 1, 2026, 12:54 a.m.