Triple
T29528449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lark |
E749130
|
entity |
| Predicate | hasDynamicsCharacteristic |
P44121
|
FINISHED |
| Object | predominantly soft dynamics |
—
|
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: predominantly soft dynamics | Statement: [The Lark, hasDynamicsCharacteristic, predominantly soft dynamics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDynamicsCharacteristic Context triple: [The Lark, hasDynamicsCharacteristic, predominantly soft dynamics]
-
A.
hasDynamics
Indicates that one entity exhibits or is characterized by specific dynamic behavior, changes, or variations over time in relation to another entity or context.
-
B.
hasPowerDynamic
Indicates that there is an imbalance or structured hierarchy of influence, control, or authority between the related entities.
-
C.
hasMusicCharacteristic
chosen
Indicates that an entity possesses a specific musical feature, quality, or attribute.
-
D.
hasKinetics
Indicates that one entity is associated with the kinetic properties or rate-related behavior of another entity in a process or reaction.
-
E.
hasCharacteristic
Indicates that an entity possesses, exhibits, or is defined by a particular attribute, feature, or quality.
- 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_69f0bd46d99c81908ba9d01cc1dbef7d |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69f66d7765208190b87b1cc6d96a151c |
completed | May 2, 2026, 9:32 p.m. |
| PD | Predicate disambiguation | batch_69f66abfdaf08190a55f14c70be6fd4d |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 28, 2026, 4:49 p.m.