Triple
T1069353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maryland Senate |
E23287
|
entity |
| Predicate | regularSessionStart |
P1309
|
FINISHED |
| Object | second Wednesday in January |
—
|
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: second Wednesday in January | Statement: [Maryland Senate, regularSessionStart, second Wednesday in January]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regularSessionStart Context triple: [Maryland Senate, regularSessionStart, second Wednesday in January]
-
A.
firstSessionStart
Indicates the point in time when an entity’s very first session or interaction begins.
-
B.
secondSessionStart
Indicates the point in time when a second or subsequent session begins.
-
C.
convenesRegularSession
chosen
Indicates that an entity formally brings together a group or body for its routine or scheduled meeting.
-
D.
sessionType
Indicates the classification or category of a particular session based on its purpose, format, or context.
-
E.
sessionLength
Indicates the duration of time that a particular session lasts from start to end.
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b914b4908190886d6698294c6b5b |
completed | March 1, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69a4b73844708190a16c9e9824ca2fb6 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.