Triple
T35631999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Techron fuel additive |
E1029611
|
entity |
| Predicate | recommendedUsageInterval |
P183562
|
FINISHED |
| Object | periodic use as part of maintenance |
—
|
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: periodic use as part of maintenance | Statement: [Techron fuel additive, recommendedUsageInterval, periodic use as part of maintenance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recommendedUsageInterval Context triple: [Techron fuel additive, recommendedUsageInterval, periodic use as part of maintenance]
-
A.
recommendedUsageFrequency
Indicates how often something is advised or prescribed to be used within a given time period.
-
B.
recommendedUsageTime
Indicates the period of time for which something is advised or intended to be used.
-
C.
dosingInterval
Indicates the time period that should elapse between consecutive doses of a medication or treatment.
-
D.
serviceFrequencyType
Indicates how often a service occurs or is scheduled within a given time period.
-
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_69f76e07bb0c8190968ea2d836fc42c9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a01efcc08190bba489a9099b8684 |
completed | May 3, 2026, 7:21 p.m. |
| PD | Predicate disambiguation | batch_69f79e4d885881908a3612e2e75cf84f |
completed | May 3, 2026, 7:13 p.m. |
| PDg | Predicate description generation | batch_69f79f477c4c8190a35cb6d87b1dcbd1 |
completed | May 3, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:05 p.m.