Triple
T289620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Insectivorous Plants |
E5959
|
entity |
| Predicate | hasPageCountApprox |
P9415
|
FINISHED |
| Object | 462 |
—
|
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: 462 | Statement: [Insectivorous Plants, hasPageCountApprox, 462]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPageCountApprox Context triple: [Insectivorous Plants, hasPageCountApprox, 462]
-
A.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
B.
hasTotalNumber
Indicates that an entity is associated with a specific overall count or sum of items, elements, or units.
-
C.
hasApproximateVendorCount
Indicates that an entity is associated with an estimated or non-exact number of vendors.
-
D.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
E.
approximateNumberOfVerses
Indicates an estimated or approximate count of verses associated with an entity.
- 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_69a25946a7ac8190a78871c210213272 |
completed | Feb. 28, 2026, 2:56 a.m. |
| NER | Named-entity recognition | batch_69a25e4df5508190ab75115ac6b3964e |
completed | Feb. 28, 2026, 3:17 a.m. |
| PD | Predicate disambiguation | batch_69a25b7d089081909810442c3d0182f6 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25d0ec71081908478c800be4f7bb0 |
completed | Feb. 28, 2026, 3:12 a.m. |
Created at: Feb. 28, 2026, 3:02 a.m.