Triple
T21531256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalends Nones Ides |
E531234
|
entity |
| Predicate | referencePointsPerMonth |
P144730
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Kalends Nones Ides, referencePointsPerMonth, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: referencePointsPerMonth Context triple: [Kalends Nones Ides, referencePointsPerMonth, 3]
-
A.
numberOfRecipientsPerMonth
Indicates the quantity of recipients associated with an entity for each month.
-
B.
peakMonthlyActiveUsers
Indicates the highest number of distinct users who were active within any single month over a given period.
-
C.
typicalMonthOfOccurrence
Indicates the month in which something most commonly or typically occurs.
-
D.
tryPoints
Indicates an attempt by one entity to score or gain points, typically through some action or effort directed toward achieving those points.
-
E.
appointmentFrequency
Indicates how often appointments are scheduled or expected to occur within a given time period.
- 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_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d08ebf881909574e098404f93fa |
completed | April 26, 2026, 11:17 p.m. |
| PD | Predicate disambiguation | batch_69e6320043bc81909417c41a718652ba |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e6386c5a4481909c37f7de7e9fc025 |
completed | April 20, 2026, 2:30 p.m. |
Created at: April 16, 2026, 6:27 p.m.