Triple
T215932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | red kangaroo |
E4104
|
entity |
| Predicate | femaleWeight |
P1575
|
FINISHED |
| Object | about 20 to 40 kilograms |
—
|
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 20 to 40 kilograms | Statement: [red kangaroo, femaleWeight, about 20 to 40 kilograms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleWeight Context triple: [red kangaroo, femaleWeight, about 20 to 40 kilograms]
-
A.
averageWeight
Indicates the typical or mean weight value associated with an entity or group of entities.
-
B.
weight
chosen
Indicates a relationship where a numerical value quantifies how heavy an entity is, often used to measure or compare mass or load.
-
C.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
D.
numberOfFemaleAthletes
Indicates the count of athletes who are female in a given context or group.
-
E.
womenWing
Indicates a relationship where a woman is associated with or positioned at the wing (side section) of a structure, group, or setting.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b52190481908f299d26122bafd2 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.