Triple
T28642734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balloon Dog |
E724970
|
entity |
| Predicate | seriesMemberCount |
P80358
|
FINISHED |
| Object | multiple versions |
—
|
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: multiple versions | Statement: [Balloon Dog, seriesMemberCount, multiple versions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seriesMemberCount Context triple: [Balloon Dog, seriesMemberCount, multiple versions]
-
A.
fictionalMembersCount
Indicates the number of fictional entities that are members of a given group or collection.
-
B.
hasNumberedMembers
Indicates that a group or collection has members that are explicitly assigned distinct numerical identifiers.
-
C.
seriesSize
chosen
Indicates the total number of items or installments that make up a complete series.
-
D.
numberOfMembersDeterminedBy
Indicates that the quantity of members in one entity is defined or constrained by another entity or factor.
-
E.
filmSeriesCount
Indicates the number of films that belong to a particular film series.
- 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_69f01d8423888190bd2f4e52605bf261 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f6e6029a10819098ff21f58079e70e |
completed | May 3, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d5e8188190b1e1c2e5d1b77031 |
completed | May 3, 2026, 5:57 a.m. |
Created at: April 28, 2026, 4:46 a.m.