Triple
T29007488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eight hours labour, eight hours recreation, eight hours rest |
E736474
|
entity |
| Predicate | timeDivision |
P144448
|
FINISHED |
| Object | 24-hour day |
—
|
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: 24-hour day | Statement: [Eight hours labour, eight hours recreation, eight hours rest, timeDivision, 24-hour day]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeDivision Context triple: [Eight hours labour, eight hours recreation, eight hours rest, timeDivision, 24-hour day]
-
A.
numberOfTimeSlotsPerCarrier
Indicates the quantity of discrete time slots that are allocated or assigned to each individual carrier.
-
B.
timeSharingWith
chosen
Indicates a relationship where two or more entities are concurrently or alternately using, accessing, or occupying the same resource, system, or period of time.
-
C.
divisionFrequency
Indicates how often a division event occurs within a given context or time frame.
-
D.
timeDomain
Indicates that something is characterized, defined, or analyzed with respect to time rather than another domain (such as frequency or space).
-
E.
usesFrequencyReuse
Indicates that one entity applies the technique of reusing the same frequency channels across different locations or cells within a system or network.
- 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_69f077eb81e88190ad9ff62cbb9f555e |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f65fd9cb788190beb90acc39f381b1 |
completed | May 2, 2026, 8:34 p.m. |
| PD | Predicate disambiguation | batch_69f659d297cc8190b2b962ba30a1edb3 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 28, 2026, 9:39 a.m.