Triple
T13395469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiliaceae |
E319690
|
entity |
| Predicate | hasEconomicImportanceVia |
P74963
|
FINISHED |
| Object | Tilia wood |
—
|
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: Tilia wood | Statement: [Tiliaceae, hasEconomicImportanceVia, Tilia wood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEconomicImportanceVia Context triple: [Tiliaceae, hasEconomicImportanceVia, Tilia wood]
-
A.
hasEconomicImportanceFor
Indicates that one entity holds economic value, benefit, or significance for another entity.
-
B.
hasCommercialImportance
chosen
Indicates that something possesses economic or business value significant enough to impact trade, revenue, or market activity.
-
C.
hasMainEconomicUse
Indicates that something serves as the primary or principal economic function, purpose, or use of another entity.
-
D.
hasIndustrialSignificance
Indicates that something plays an important role or has notable impact within industrial processes, production, or applications.
-
E.
hasEconomicInterest
Indicates that one entity stands to gain or lose financially or materially from the performance, decisions, or outcomes associated with another entity.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dba0d892d08190b1b192b93fe3d72d |
completed | April 12, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69d9a03189908190a784a2755f8d81e1 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:34 p.m.