Triple
T868982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercury |
E18768
|
entity |
| Predicate | density_mean |
P2023
|
FINISHED |
| Object | about 5.43 grams per cubic centimeter |
—
|
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: about 5.43 grams per cubic centimeter | Statement: [Mercury, density_mean, about 5.43 grams per cubic centimeter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: density_mean Context triple: [Mercury, density_mean, about 5.43 grams per cubic centimeter]
-
A.
hasMeanDensity
chosen
Indicates that one entity possesses a specified average mass per unit volume (mean density).
-
B.
clusterDensity
Indicates the degree to which elements within a cluster are closely packed or concentrated relative to its size or volume.
-
C.
hasPopulationDensity
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
D.
hasPopulationCenterDensity
Indicates the density of population centers within a given area or region.
-
E.
populationDensity
Indicates the number of individuals or entities occupying a unit area within a given region.
- 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_69a4938ce8688190a24bdfef82ba7d21 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac7f98908190883f71049092c7c8 |
completed | March 1, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69a4aa87504481909618a6815948da6f |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.