Triple
T22462187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pastis 51 |
E555256
|
entity |
| Predicate | typicalDilutionRatio |
P112061
|
FINISHED |
| Object | 1 part pastis to 5 parts water |
—
|
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: 1 part pastis to 5 parts water | Statement: [Pastis 51, typicalDilutionRatio, 1 part pastis to 5 parts water]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDilutionRatio Context triple: [Pastis 51, typicalDilutionRatio, 1 part pastis to 5 parts water]
-
A.
typicalIngredientRatio
chosen
Indicates the usual proportional relationship between different ingredients used together in a preparation or mixture.
-
B.
typicalBottleCount
Indicates the usual or standard number of bottles associated with something under normal conditions.
-
C.
concentrationParameterAffects
Indicates that a change in a concentration parameter has an influence or impact on another variable, condition, or outcome.
-
D.
typicalInfusionCount
Indicates the usual or standard number of infusions associated with a given treatment or protocol.
-
E.
typicalUseRatio
Indicates the proportion or share in which something is commonly or normally used relative to other possible uses or components.
- 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_69e11e51fdec8190adfdf9f8a6362221 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b80983081908084947b2e31c9d4 |
completed | April 29, 2026, 1:14 a.m. |
| PD | Predicate disambiguation | batch_69e898ad961c819098fd1e46129bddcc |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:48 p.m.