Triple
T215930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | red kangaroo |
E4104
|
entity |
| Predicate | tailLength |
P266
|
FINISHED |
| Object | about 1.0 to 1.2 metres for males |
—
|
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 1.0 to 1.2 metres for males | Statement: [red kangaroo, tailLength, about 1.0 to 1.2 metres for males]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tailLength Context triple: [red kangaroo, tailLength, about 1.0 to 1.2 metres for males]
-
A.
length
chosen
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
B.
tailConfiguration
Indicates how the tail of an entity is arranged, structured, or positioned relative to the rest of that entity.
-
C.
hasCarLength
Indicates that an entity is associated with a specific measurement representing the length of a car.
-
D.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
-
E.
maximumTrunkDiameter
Indicates the largest thickness of a trunk measured across its widest point.
- 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.