Triple
T31469458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Segeberger Kalkberg caves |
E802813
|
entity |
| Predicate | approximateBatCount |
P196952
|
FINISHED |
| Object | tens of thousands |
—
|
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: tens of thousands | Statement: [Segeberger Kalkberg caves, approximateBatCount, tens of thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateBatCount Context triple: [Segeberger Kalkberg caves, approximateBatCount, tens of thousands]
-
A.
hasApproximateNumberOfRats
Indicates that an entity is associated with an estimated or imprecise count of rats rather than an exact number.
-
B.
mineCountApproximate
Indicates that the number of mines associated with an entity is estimated or roughly counted rather than known exactly.
-
C.
approximateNumberOfZebra
Indicates that one entity specifies an estimated or approximate count of zebras associated with another entity.
-
D.
hasApproximateCountInHumans
Indicates that an entity is associated with an estimated or approximate numerical count specifically in humans.
-
E.
hasApproximateNumberOfPeople
Indicates that an entity is associated with an estimated or approximate count of people, rather than an exact number.
- 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_69f348c84c1c81908739f100ecf7394e |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe6fea4a288190bf8615c5d6bf41b4 |
completed | May 8, 2026, 11:21 p.m. |
| PD | Predicate disambiguation | batch_69fe6f774de08190975a2393b9a1fd22 |
completed | May 8, 2026, 11:19 p.m. |
| PDg | Predicate description generation | batch_69fe6fe98e38819085100ce4c6cee5b8 |
completed | May 8, 2026, 11:21 p.m. |
Created at: April 30, 2026, 9:25 p.m.