Triple
T28943944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Just Six Numbers |
E730529
|
entity |
| Predicate | hasNumberOfKeyConstantsDiscussed |
P111430
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Just Six Numbers, hasNumberOfKeyConstantsDiscussed, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfKeyConstantsDiscussed Context triple: [Just Six Numbers, hasNumberOfKeyConstantsDiscussed, 6]
-
A.
hasKeyQuestion
Indicates that one entity possesses or is associated with a primary or central question relevant to another entity.
-
B.
typicalKeyCount
chosen
Indicates the usual or standard number of keys associated with an entity in this context.
-
C.
hasKVNumber
Indicates that an entity is associated with a specific KV (kilovolt) identification number, typically used to label or reference electrical equipment or lines.
-
D.
hasNumberOfTerms
Indicates the quantity of distinct terms or elements associated with a given entity or expression.
-
E.
numberOfDialogues
Indicates the total count of dialogues associated with or occurring between the referenced entities.
- 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_69f043ea0aa88190a25acbf46157995a |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69fcec5f8b448190b48330a19b462d24 |
completed | May 7, 2026, 7:47 p.m. |
| PD | Predicate disambiguation | batch_69fceaf1e23881908ca24160a638e329 |
completed | May 7, 2026, 7:41 p.m. |
Created at: April 28, 2026, 8:38 a.m.