Triple
T198253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mango |
E4043
|
entity |
| Predicate | hasSeedCount |
P8130
|
FINISHED |
| Object | single seed |
—
|
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: single seed | Statement: [Mango, hasSeedCount, single seed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeedCount Context triple: [Mango, hasSeedCount, single seed]
-
A.
hasSeeding
Indicates that an entity is assigned or associated with a specific seeding position or rank, typically for ordering or placement in a competitive or structured context.
-
B.
usesSeeding
Indicates that an entity employs a seeding process or strategy to initiate, distribute, or propagate something (such as data, content, or resources).
-
C.
hasNumberOfDrops
Indicates the quantity or count of drops associated with an entity or event.
-
D.
seedType
Indicates the specific kind or category of seed associated with an entity.
-
E.
hasSectionCount
Indicates that an entity is associated with a specific number of sections it contains or comprises.
- F. None of above. chosen
Provenance (4 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25be47ea881909c296b30a0d47a65 |
completed | Feb. 28, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69a25b47481c8190add47c641c977bb9 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25be349588190aedde33d80682344 |
completed | Feb. 28, 2026, 3:07 a.m. |
Created at: Feb. 28, 2026, 2:44 a.m.