Triple
T9362867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seder plate |
E225321
|
entity |
| Predicate | typicalNumberOfSymbolicItems |
P81866
|
FINISHED |
| Object | six |
—
|
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: six | Statement: [Seder plate, typicalNumberOfSymbolicItems, six]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfSymbolicItems Context triple: [Seder plate, typicalNumberOfSymbolicItems, six]
-
A.
slotCountTypical
chosen
Indicates the usual or standard number of slots associated with an entity under normal conditions.
-
B.
typicalSymbol
Indicates that something serves as a characteristic or commonly recognized symbol representing something else.
-
C.
hasApproximateNumberOfVarieties
Indicates that an entity is associated with an estimated or non-exact count of different varieties or types.
-
D.
variantCount
Indicates the number of distinct variants associated with a given entity or item.
-
E.
sectionCountApproximate
Indicates that the number of sections associated with an entity is known only approximately rather than as an exact count.
- 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_69ca842bdd648190904131d58620d448 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd503ddf8c81908b090afa54ec5e6d |
completed | April 1, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_69cc7a6abb8c81908c7a2f4ee92cc949 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:42 p.m.