Triple
T6287065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Galactic thin disk |
E140925
|
entity |
| Predicate | hasChemicalGradient |
P63610
|
FINISHED |
| Object | negative radial metallicity gradient |
—
|
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: negative radial metallicity gradient | Statement: [Galactic thin disk, hasChemicalGradient, negative radial metallicity gradient]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChemicalGradient Context triple: [Galactic thin disk, hasChemicalGradient, negative radial metallicity gradient]
-
A.
hasGradient
chosen
Indicates that one entity possesses or is characterized by a gradual change in value, intensity, or property across its extent or between two points.
-
B.
hasChemicalCluster
Indicates that one entity contains, is associated with, or is organized into a specific group or cluster of chemically related components or structures.
-
C.
hasChemicalClass
Indicates that an entity belongs to, or is categorized under, a particular chemical class based on its structural or compositional characteristics.
-
D.
averageGradient
Indicates the mean rate of change (slope) of a quantity over a specified interval or region.
-
E.
usesGradientInformation
Indicates that an entity performs its operation by leveraging gradient (derivative) information, typically to guide optimization or learning steps.
- 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_69c008cd17c8819082b82d3fbeb68047 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063ff9f74819088dc603f56fc930c |
completed | March 22, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69c0560a0270819098ad2785b91e8f39 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:26 p.m.