Triple
T16055404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hard Piano |
E389465
|
entity |
| Predicate | hasDifficultyCharacteristic |
P2406
|
FINISHED |
| Object | challenging |
—
|
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: challenging | Statement: [Hard Piano, hasDifficultyCharacteristic, challenging]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDifficultyCharacteristic Context triple: [Hard Piano, hasDifficultyCharacteristic, challenging]
-
A.
hasDifficultyContext
Indicates that something’s difficulty is defined, interpreted, or constrained within a particular situational or contextual framework.
-
B.
hasDifficultyEffect
Indicates that one entity causes a change in the difficulty level or challenge associated with another entity or activity.
-
C.
hasDifficultyWith
Indicates that one entity experiences problems, challenges, or lack of proficiency in dealing with, understanding, or performing something related to another entity.
-
D.
hasMeasurementDifficulty
Indicates that performing a measurement on something is challenging or problematic in some way.
-
E.
difficulty
chosen
Indicates the level of challenge, complexity, or effort required to perform an action, solve a problem, or achieve a particular outcome.
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1858a00888190b8505071575dc56f |
completed | April 17, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e18272f2288190a17d45fb01cc2b07 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:56 a.m.