Triple
T215928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | red kangaroo |
E4104
|
entity |
| Predicate | largest |
P4495
|
FINISHED |
| Object | largest living marsupial |
—
|
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: largest living marsupial | Statement: [red kangaroo, largest, largest living marsupial]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: largest Context triple: [red kangaroo, largest, largest living marsupial]
-
A.
isLargestOf
chosen
Indicates that one entity has the greatest size, extent, or magnitude among a specified set of entities.
-
B.
largestCity
Indicates that one city is the most populous or significant urban center within a specified region or entity.
-
C.
isLargestCityIn
Indicates that one city has the greatest population or size compared to all other cities within a specified region or administrative area.
-
D.
largestMetropolitanArea
Indicates that one entity is the largest metropolitan area associated with, contained within, or relevant to another entity, typically by population or spatial extent.
-
E.
largestAirport
Indicates that one airport is the largest (typically by area, traffic, or capacity) among a specified set or 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_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.