Triple
T26514312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nordmann fir |
E669769
|
entity |
| Predicate | evergreenNeedleRetention |
P156525
|
FINISHED |
| Object | good needle retention after cutting |
—
|
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: good needle retention after cutting | Statement: [Nordmann fir, evergreenNeedleRetention, good needle retention after cutting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: evergreenNeedleRetention Context triple: [Nordmann fir, evergreenNeedleRetention, good needle retention after cutting]
-
A.
evergreenHabit
Indicates that a plant maintains its foliage year-round rather than shedding leaves seasonally.
-
B.
evergreen
Indicates that something remains persistently relevant, active, or unchanged over time, without becoming outdated or obsolete.
-
C.
leafLongevity
chosen
Indicates the duration for which a leaf remains alive and functional before it senesces or is shed.
-
D.
foliageCharacteristic
Indicates the specific traits or qualities of an entity’s foliage, such as its type, texture, color, or other distinguishing features.
-
E.
treeLongevity
Indicates the duration or lifespan of a tree, typically measured from planting or germination to death or removal.
- 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_69eeb31b6dcc8190b30632dc3928a0c0 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f61394b50c81909e628b2e5b1aa3d5 |
completed | May 2, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69f602d5c8808190a1fdbebd6f0981e8 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 27, 2026, 1:22 a.m.