Triple
T11920150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheddar cheese |
E283630
|
entity |
| Predicate | meltingQuality |
P102361
|
FINISHED |
| Object | good melting cheese |
—
|
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: good melting cheese | Statement: [Cheddar cheese, meltingQuality, good melting cheese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meltingQuality Context triple: [Cheddar cheese, meltingQuality, good melting cheese]
-
A.
materialMelted
Indicates that a material has undergone melting, transitioning from a solid to a liquid state.
-
B.
canMelt
Indicates that one entity has the capability to melt another entity or substance under appropriate conditions.
-
C.
hasMeltingMechanism
Indicates that an entity possesses a specific mechanism or process by which it melts or causes melting.
-
D.
isNonMelting
Indicates that the subject does not melt or transition to a liquid state under the relevant or specified conditions.
-
E.
isMeltingCheese
Indicates that one entity is causing cheese to transition from a solid to a softened or liquid state through heating or similar means.
- 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e8dff77481908cacf6ad03df34ac |
completed | April 10, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3632ac8190b13e53c2b5db7125 |
completed | April 10, 2026, 8:56 a.m. |
| PDg | Predicate description generation | batch_69d8dd0ba0f88190b7d5e358c27ca184 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:45 p.m.