Triple
T35945493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ixerbaceae |
E1039576
|
entity |
| Predicate | hasMemberSpeciesCountApproximate |
P189432
|
FINISHED |
| Object | few |
—
|
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: few | Statement: [Ixerbaceae, hasMemberSpeciesCountApproximate, few]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMemberSpeciesCountApproximate Context triple: [Ixerbaceae, hasMemberSpeciesCountApproximate, few]
-
A.
numberOfSpecies
Indicates the count of distinct species associated with a given entity or context.
-
B.
hasLimitedSpeciesNumber
chosen
Indicates that the entity is associated with a relatively small or restricted number of species.
-
C.
speciesNumber
Indicates the numerical identifier or count associated with a particular species in a given context.
-
D.
hasApproximateCountInHumans
Indicates that an entity is associated with an estimated or approximate numerical count specifically in humans.
-
E.
hasWildPopulationOf
Indicates that a location or area contains a naturally occurring, non-captive population of the specified species.
- 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_69f76e24bbd0819096b837d35371639a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff45793d5c81909dc503ad1f714ee2 |
completed | May 9, 2026, 2:32 p.m. |
| PD | Predicate disambiguation | batch_69ff41cb0e088190a6e9b03cb20e5fad |
completed | May 9, 2026, 2:16 p.m. |
Created at: May 3, 2026, 4:07 p.m.