Triple
T34409220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viva Top 100 |
E883207
|
entity |
| Predicate | numberOfItemsInList |
P59623
|
FINISHED |
| Object | 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: 100 | Statement: [Viva Top 100, numberOfItemsInList, 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfItemsInList Context triple: [Viva Top 100, numberOfItemsInList, 100]
-
A.
estimatedNumberOfItems
Indicates the approximate count or quantity of items associated with the subject, typically when the exact number is unknown or uncertain.
-
B.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
C.
collectionSize
Indicates the total number of items contained within a specified collection.
-
D.
numberOfEntries
chosen
Indicates the total count of individual items, records, or instances associated with a given entity or context.
-
E.
numberOfListSeats
Indicates the total count of seats allocated from a party or group list within a representative body or election system.
- 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_69f349c1f2208190a09a489bb8b2719d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fbe4a08190bfe65ebd141164e9 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:59 a.m.