Triple
T184517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International Court of Arbitration for the Flower Trade |
E3951
|
entity |
| Predicate | sectorSpecialization |
P2193
|
FINISHED |
| Object | flower industry |
—
|
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: flower industry | Statement: [International Court of Arbitration for the Flower Trade, sectorSpecialization, flower industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectorSpecialization Context triple: [International Court of Arbitration for the Flower Trade, sectorSpecialization, flower industry]
-
A.
sectorServed
chosen
Indicates the industry or economic sector that an entity primarily serves or targets with its activities, products, or services.
-
B.
sector
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
C.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
D.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25926be9c8190a4cfce66f57589d1 |
completed | Feb. 28, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69a2566fb08c81908faff2fde552105d |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.