Triple
T59868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Legislature |
E1186
|
entity |
| Predicate | regularSessionFrequency |
P2557
|
FINISHED |
| Object | every two years |
—
|
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: every two years | Statement: [Texas Legislature, regularSessionFrequency, every two years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regularSessionFrequency Context triple: [Texas Legislature, regularSessionFrequency, every two years]
-
A.
meetingFrequency
chosen
Indicates how often a meeting or recurring gathering takes place over a given period.
-
B.
convenesRegularSession
Indicates that an entity formally brings together a group or body for its routine or scheduled meeting.
-
C.
typicalSchedule
Indicates the usual or standard timing and sequence of activities or events associated with an entity.
-
D.
awardedFrequency
Indicates how often an award or recognition is given within a specified time period.
-
E.
meetsEvery
Indicates that one entity encounters or comes into contact with every member of a specified set of entities.
- 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_69a24a552ef88190a0df287d68c65cba |
completed | Feb. 28, 2026, 1:52 a.m. |
| NER | Named-entity recognition | batch_69a250e401288190ba12322c9c5f07c9 |
completed | Feb. 28, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69a24e9f40908190a2f4a2111469b733 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 1:55 a.m.