Triple
T23009679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 59 Cygni |
E572871
|
entity |
| Predicate | hasRadialVelocityCurve |
P62576
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [59 Cygni, hasRadialVelocityCurve, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRadialVelocityCurve Context triple: [59 Cygni, hasRadialVelocityCurve, yes]
-
A.
hasRadialVelocityMeasurement
Indicates that a radial velocity value has been measured for an entity, typically quantifying its motion along the line of sight relative to an observer.
-
B.
hasRadialVelocitySignature
chosen
Indicates that an object exhibits a measurable radial velocity pattern characteristic of a specific physical process or source.
-
C.
hasRadialVelocity_km_per_s
Indicates that one entity has a measured radial velocity, expressed in kilometers per second, relative to another reference frame or object.
-
D.
hasRotationCurve
Indicates that an object is associated with a rotation curve describing how its rotational velocity varies with radius or position.
-
E.
hasRadialVelocityDispersion
Indicates that an entity exhibits a spread in its radial velocities, quantifying how much the line-of-sight speeds of its components differ from one another.
- 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_69e245b764cc8190a51be76f1d9611e1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18359f5548190b15a139eb09e30a0 |
completed | April 29, 2026, 4:04 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9cd5488190bcd23183179f48cd |
completed | April 27, 2026, 10:34 a.m. |
Created at: April 17, 2026, 3:51 p.m.