Triple
T38122921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Miller |
E951989
|
entity |
| Predicate | drunkennessAffects |
P63613
|
FINISHED |
| Object | his decision to tell his tale out of turn |
—
|
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: his decision to tell his tale out of turn | Statement: [The Miller, drunkennessAffects, his decision to tell his tale out of turn]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drunkennessAffects Context triple: [The Miller, drunkennessAffects, his decision to tell his tale out of turn]
-
A.
drunkWith
Indicates that one entity is intoxicated as a result of consuming a particular alcoholic beverage or substance associated with another entity.
-
B.
drunkBy
Indicates that a substance or beverage is consumed by a particular entity.
-
C.
diesAfterDrinking
Indicates that an entity dies as a consequence of having previously consumed a drink.
-
D.
effectOnDrinkers
chosen
Indicates the impact or consequences that something has on individuals who consume alcoholic beverages.
-
E.
isTypicallyDrunk
Indicates that the subject is most often or commonly consumed in a liquid form by drinking.
- 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 3, 2026, 4:21 p.m.