Triple
T26721916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SR 100 |
E673726
|
entity |
| Predicate | mayHaveMultipleInstances |
P125807
|
FINISHED |
| Object | in different U.S. states simultaneously |
—
|
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: in different U.S. states simultaneously | Statement: [SR 100, mayHaveMultipleInstances, in different U.S. states simultaneously]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayHaveMultipleInstances Context triple: [SR 100, mayHaveMultipleInstances, in different U.S. states simultaneously]
-
A.
hasMultiplePhysicalInstances
Indicates that a single conceptual entity is realized or exists as more than one distinct physical instance.
-
B.
hasMultiple
Indicates that an entity is associated with more than one instance or occurrence of another related entity.
-
C.
canBeMultiple
chosen
Indicates that the related item, value, or association is allowed to occur more than once rather than being restricted to a single instance.
-
D.
hasMultipleCreations
Indicates that an entity is associated with more than one distinct creation or produced work.
-
E.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
- 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_69eecda481d08190aea69f2f7c745f56 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6247480cc8190a887eedaeb94615c |
completed | May 2, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69f623a7539c8190b71797f583da9f63 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 3:41 a.m.