Triple
T19810324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinus jeffreyi |
E475925
|
entity |
| Predicate | typicalTrunkDiameter |
P66461
|
FINISHED |
| Object | 0.6–1.5 metres |
—
|
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: 0.6–1.5 metres | Statement: [Pinus jeffreyi, typicalTrunkDiameter, 0.6–1.5 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTrunkDiameter Context triple: [Pinus jeffreyi, typicalTrunkDiameter, 0.6–1.5 metres]
-
A.
maximumTrunkDiameter
Indicates the largest thickness of a trunk measured across its widest point.
-
B.
trunkDiameter
chosen
Indicates the measured thickness of a trunk, typically expressed as the diameter across its cross-section.
-
C.
treeHabit
Indicates the growth form or structural habit characteristic of a tree, such as its typical shape, branching pattern, or overall stature.
-
D.
typicalCaseDiameter
Indicates the usual or standard diameter value associated with an object or case in typical conditions.
-
E.
bodyDiameter
Indicates the measurement of how wide an object's body is across its broadest cross-section.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6542bd7a48190acf67db41f1131c9 |
completed | April 20, 2026, 4:28 p.m. |
| PD | Predicate disambiguation | batch_69e5305858108190bbbfdb9ba3ab9f80 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:50 p.m.