Triple
T896263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haumea |
E19352
|
entity |
| Predicate | density |
P2023
|
FINISHED |
| Object | about 1.9 to 2.0 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 1.9 to 2.0 grams per cubic centimeter | Statement: [Haumea, density, about 1.9 to 2.0 grams per cubic centimeter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: density Context triple: [Haumea, density, about 1.9 to 2.0 grams per cubic centimeter]
-
A.
hasMeanDensity
chosen
Indicates that one entity possesses a specified average mass per unit volume (mean density).
-
B.
depth
Indicates the vertical distance from a reference surface or top point down to a lower point or bottom within a medium or space.
-
C.
clusterDensity
Indicates the degree to which elements within a cluster are closely packed or concentrated relative to its size or volume.
-
D.
dimension
Indicates that one entity specifies a measurable extent or size attribute (such as length, width, height, or similar quantitative property) of another entity.
-
E.
thickness
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
- 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_69a4939d37188190848be3d426ebc9ae |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad23d6e88190a2fb5e1e168a7b44 |
completed | March 1, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69a4aa94f7c881908deeb62308942e19 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.