Triple
T5509450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heineken lager beer |
E144525
|
entity |
| Predicate | bitterness |
P38317
|
FINISHED |
| Object | moderate |
—
|
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: moderate | Statement: [Heineken lager beer, bitterness, moderate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bitterness Context triple: [Heineken lager beer, bitterness, moderate]
-
A.
hasBitternessLevel
chosen
Indicates that an entity is associated with a specific degree or intensity of bitterness.
-
B.
correctsAberration
Indicates that one entity counteracts, fixes, or compensates for an error, flaw, or deviation present in another entity.
-
C.
dislikes
Indicates that one entity has a negative feeling or aversion toward another entity.
-
D.
negativeType
Indicates that one entity is classified as a negative, undesirable, or disfavored type in relation to another or within a given context.
-
E.
bias
Indicates a systematic preference or prejudice in favor of or against an entity, affecting how it is treated, evaluated, or represented relative to others.
- 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f4ba90c8190ad22e5de84c545f9 |
completed | March 22, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69c01b07bde08190b3933b96bdc70dd5 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:33 p.m.