Triple
T530201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sivan |
E12204
|
entity |
| Predicate | timeUnitType |
P6008
|
FINISHED |
| Object | month of year |
—
|
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: month of year | Statement: [Sivan, timeUnitType, month of year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeUnitType Context triple: [Sivan, timeUnitType, month of year]
-
A.
timeType
Indicates the specific temporal category or classification associated with a time-related entity or value (e.g., duration, point in time, interval, or recurrence type).
-
B.
typeOfUnit
chosen
Indicates that one entity specifies the kind or category of measurement unit that the other entity belongs to.
-
C.
timePeriod
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
D.
timeEquivalentOf
Indicates that two temporal entities represent the same point in time or duration, possibly expressed in different formats or units.
-
E.
timeScaleType
Indicates the type or category of temporal scaling applied to an event, process, or measurement (e.g., real-time, accelerated, aggregated).
- 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_69a4933208e88190891f5debab1b776d |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4985e51908190a34aa82ea9dbee1e |
completed | March 1, 2026, 7:49 p.m. |
| PD | Predicate disambiguation | batch_69a494b257108190a537dffbb9d621b5 |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:32 p.m.