Triple
T38027226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arashiyama Monkey Park Iwatayama |
E948809
|
entity |
| Predicate | approximateNumberOfMonkeys |
P120939
|
FINISHED |
| Object | over 100 |
—
|
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: over 100 | Statement: [Arashiyama Monkey Park Iwatayama, approximateNumberOfMonkeys, over 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfMonkeys Context triple: [Arashiyama Monkey Park Iwatayama, approximateNumberOfMonkeys, over 100]
-
A.
hasApproximateNumberOfMonkeys
chosen
Indicates that an entity is associated with an estimated or non-exact count of monkeys.
-
B.
approximateBatCount
Indicates an estimated number of bats associated with or observed at a given entity or event.
-
C.
hasApproximateNumberOfRats
Indicates that an entity is associated with an estimated or imprecise count of rats rather than an exact number.
-
D.
approximateNumberOfZebra
Indicates that one entity specifies an estimated or approximate count of zebras associated with another entity.
-
E.
numberOfMonksApprox
Indicates an approximate count or estimate of how many monks are involved or present in a given context.
- 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_69f76efd1bc48190a729097fe5177b61 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ffebbd9bac8190b3dca4b7252a2278 |
completed | May 10, 2026, 2:21 a.m. |
| PD | Predicate disambiguation | batch_69ffe93120a08190a44bb64d052eda78 |
completed | May 10, 2026, 2:10 a.m. |
Created at: May 3, 2026, 4:20 p.m.