Triple
T14778943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pongo |
E347339
|
entity |
| Predicate | adoptiveChildrenCount |
P88684
|
FINISHED |
| Object | 84 adopted puppies (in the original story) |
—
|
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: 84 adopted puppies (in the original story) | Statement: [Pongo, adoptiveChildrenCount, 84 adopted puppies (in the original story)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adoptiveChildrenCount Context triple: [Pongo, adoptiveChildrenCount, 84 adopted puppies (in the original story)]
-
A.
numberOfAdoptedChildren
chosen
Indicates the count of children that an entity has legally adopted.
-
B.
adoptedChildren
Indicates that one entity has legally taken another entity as their child through adoption.
-
C.
adoptiveChild
Indicates that one entity is the child of another through legal adoption rather than biological descent.
-
D.
adoptedChildrenFrom
Indicates a relationship where one entity has legally adopted children originating from another entity (such as a person, couple, or organization).
-
E.
adoptiveParent
Indicates that one entity is the legally recognized parent of another through adoption rather than biological descent.
- 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_69d822e9b9e08190bedcc31a163fda82 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deca9c7cac8190ba900df95e42e318 |
completed | April 14, 2026, 11:15 p.m. |
| PD | Predicate disambiguation | batch_69de8c02e5c08190943c27594026faf7 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:31 a.m.