Triple
T25290205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kerium Anti-Hairloss Intensive Treatment |
E634060
|
entity |
| Predicate | hasApplicationFrequency |
P151281
|
FINISHED |
| Object | regular repeated application |
—
|
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: regular repeated application | Statement: [Kerium Anti-Hairloss Intensive Treatment, hasApplicationFrequency, regular repeated application]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApplicationFrequency Context triple: [Kerium Anti-Hairloss Intensive Treatment, hasApplicationFrequency, regular repeated application]
-
A.
hasEventFrequency
Indicates how often a particular event occurs within a given time period.
-
B.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
C.
hasFrequencyCategory
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
D.
isAdministeredFrequency
chosen
Indicates how often an action, treatment, or process is administered over a given period.
-
E.
canObserveFrequency
Indicates that one entity has the capability to detect, measure, or monitor the frequency of another entity or signal.
- 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_69e75a9503d48190b80a005c6af0cb50 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f6135293908190809e255bf6334760 |
completed | May 2, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69f611a72780819082f44e66ca2c6ac9 |
completed | May 2, 2026, 3 p.m. |
Created at: April 21, 2026, 1:21 p.m.