Triple
T9849028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Education Committee (Arizona House of Representatives) |
E239416
|
entity |
| Predicate | hasSubstantiveArea |
P90344
|
FINISHED |
| Object | education |
—
|
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: education | Statement: [Education Committee (Arizona House of Representatives), hasSubstantiveArea, education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubstantiveArea Context triple: [Education Committee (Arizona House of Representatives), hasSubstantiveArea, education]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasMacroArea
Indicates that one entity belongs to, or is located within, a broader geographic or conceptual macro-area represented by another entity.
-
C.
hasCivilArea
Indicates that an administrative or political entity encompasses or is associated with a specific civil (local administrative) area.
-
D.
hasCoreArea
Indicates that an entity possesses a primary or central area that is fundamental to its structure, function, or focus.
-
E.
hasAreaTotal
Indicates the total surface area associated with an entity, typically measured over its entire extent.
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb371894c8190971ba497a2801521 |
completed | April 2, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69cd03e57cac8190914bb5ae608a6e0e |
completed | April 1, 2026, 11:39 a.m. |
| PDg | Predicate description generation | batch_69cd06ace53081909b5f81f382f6591e |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:34 p.m.