Triple
T129720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massachusetts Governor's Council |
E2626
|
entity |
| Predicate | meetsFrequency |
P2557
|
FINISHED |
| Object | regularly during the year, typically weekly when in session |
—
|
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: regularly during the year, typically weekly when in session | Statement: [Massachusetts Governor's Council, meetsFrequency, regularly during the year, typically weekly when in session]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meetsFrequency Context triple: [Massachusetts Governor's Council, meetsFrequency, regularly during the year, typically weekly when in session]
-
A.
meetsEvery
Indicates that one entity encounters or comes into contact with every member of a specified set of entities.
-
B.
meetingFrequency
chosen
Indicates how often a meeting or recurring gathering takes place over a given period.
-
C.
meets
Indicates that two or more entities come together at the same place and time, typically for interaction or a shared purpose.
-
D.
meetsAs
Indicates that two entities encounter or come together at the same place and time, typically in a planned or recognized interaction.
-
E.
awardedFrequency
Indicates how often an award or recognition is given within a specified time period.
- 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_69a2520c0f3481908b0ed054a2fca8d0 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a2576518e0819096b35d8af7a4d1bd |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a2564da96c8190aa8204de25229c15 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:30 a.m.