Triple
T11583969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dark energy |
E274702
|
entity |
| Predicate | hasDensityParameter |
P100440
|
FINISHED |
| Object | Omega_Lambda approximately 0.7 in current best-fit models |
—
|
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: Omega_Lambda approximately 0.7 in current best-fit models | Statement: [dark energy, hasDensityParameter, Omega_Lambda approximately 0.7 in current best-fit models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDensityParameter Context triple: [dark energy, hasDensityParameter, Omega_Lambda approximately 0.7 in current best-fit models]
-
A.
hasMeanDensity
Indicates that one entity possesses a specified average mass per unit volume (mean density).
-
B.
hasSurfaceDensity
Indicates that one entity possesses or is characterized by a specific amount of mass or quantity distributed per unit area on its surface.
-
C.
givesDensityOf
Indicates that one entity provides or specifies the density value of another entity.
-
D.
hasDensityContrast
Indicates that one entity differs from another in material density, highlighting a contrast in how compact or dense they are.
-
E.
hasHighDensityOf
Indicates that one entity contains or exhibits a large concentration or amount of another entity within a given area, volume, or context.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8904db5748190ae5f10ae86ccdf46 |
completed | April 10, 2026, 5:53 a.m. |
| PD | Predicate disambiguation | batch_69d85dcbacd0819094d4a1237055affa |
completed | April 10, 2026, 2:17 a.m. |
| PDg | Predicate description generation | batch_69d87f2e67108190ac36bf47aac12fa8 |
completed | April 10, 2026, 4:40 a.m. |
Created at: April 8, 2026, 9:38 p.m.