Triple
T5737514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tapirira |
E126534
|
entity |
| Predicate | woodiness |
P55352
|
FINISHED |
| Object | woody plant |
—
|
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: woody plant | Statement: [Tapirira, woodiness, woody plant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: woodiness Context triple: [Tapirira, woodiness, woody plant]
-
A.
isWoodyPlant
chosen
Indicates that the subject is a plant characterized by persistent, woody stems or trunks that remain above ground year-round.
-
B.
hardiness
Indicates the degree to which an entity can withstand or endure harsh, adverse, or challenging conditions.
-
C.
woodProperty
Indicates that one entity specifies or characterizes a property or attribute of wood associated with another entity.
-
D.
evergreen
Indicates that something remains persistently relevant, active, or unchanged over time, without becoming outdated or obsolete.
-
E.
isConifer
Indicates that the subject is a coniferous plant, typically bearing cones and having needle-like or scale-like evergreen leaves.
- 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_69c0083082288190b7478cead6b5430a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0255c8c308190821f968ec41c5078 |
completed | March 22, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69c021c8195481909419808b002628aa |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:47 p.m.