Triple
T119812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Barrier Reef |
E2420
|
entity |
| Predicate | hasLength |
P4063
|
FINISHED |
| Object | approximately 2300 kilometres |
—
|
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: approximately 2300 kilometres | Statement: [Great Barrier Reef, hasLength, approximately 2300 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLength Context triple: [Great Barrier Reef, hasLength, approximately 2300 kilometres]
-
A.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
B.
hasMainSpanLength
chosen
Indicates the relationship specifying the primary or main span’s length associated with an entity.
-
C.
hasWidth
Indicates that an entity possesses a specific measurement or extent along its width dimension.
-
D.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
E.
hasStandardLetterCount
Indicates that an entity’s associated text or label contains a number of letters that matches a predefined standard or expected count.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a258e0b11c8190b7b5cf3c354c47ce |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a25646d5088190a057989c32da3a90 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.