Triple
T15084637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North-Western State |
E360238
|
entity |
| Predicate | numberOfSuccessorStates |
P39357
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [North-Western State, numberOfSuccessorStates, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSuccessorStates Context triple: [North-Western State, numberOfSuccessorStates, 2]
-
A.
numberOfSuccessors
chosen
Indicates the count of distinct successor entities directly following a given entity in a sequence or structure.
-
B.
successorState
Indicates that one state directly follows another as the immediate next state in a sequence or process.
-
C.
successorFunction
Indicates the relationship where one entity is defined as the immediate next or following element in a sequence or ordered set relative to another.
-
D.
successorStateFlag
Indicates that a particular state directly follows another state in a defined sequence or process.
-
E.
successorStateOperator
Indicates the operation that transforms a current state into its immediate next state according to defined transition rules.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00275cfa88190a13fe20b585d9fcb |
completed | April 15, 2026, 9:26 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:03 a.m.