Triple
T10577165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syadvada |
E249640
|
entity |
| Predicate | numberOfPredications |
P94728
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Syadvada, numberOfPredications, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPredications Context triple: [Syadvada, numberOfPredications, 7]
-
A.
numberOfPropositions
Indicates the total count of distinct propositions associated with or contained within a given entity or context.
-
B.
numberOfTruths
Indicates the quantity of statements or propositions that are true within a given context or set.
-
C.
predicateOf
Indicates that one entity functions as a predicate or predicative expression that attributes a property, relation, or condition to another entity.
-
D.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
E.
numberOfIndicators
Indicates the total count of indicators associated with or relevant to a given entity or context.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5275628988190bd00dd3f8d7c3937 |
completed | April 7, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69d51901ff6c819095e7b528170a69dc |
completed | April 7, 2026, 2:47 p.m. |
| PDg | Predicate description generation | batch_69d5270eca0481908573b698390c5b08 |
completed | April 7, 2026, 3:47 p.m. |
Created at: April 6, 2026, 12:38 p.m.