Triple
T5466643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FEMA Region IX |
E122726
|
entity |
| Predicate | numberOfFEMARegions |
P2355
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [FEMA Region IX, numberOfFEMARegions, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFEMARegions Context triple: [FEMA Region IX, numberOfFEMARegions, 10]
-
A.
numberOfRegions
chosen
Indicates the total count of distinct regions associated with or contained within a given entity.
-
B.
federalRegion
Indicates that an entity is located within, administered by, or associated with a specific federal region or federal-level administrative division.
-
C.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
D.
federalSubjectCount
Indicates the number of federal subjects (administrative units within a federation) associated with or contained by an entity.
-
E.
typicalNumberOfSuperRegionals
Indicates the usual or standard count of super-regional units or events associated with an entity.
- 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_69bd4643f16081908d7f29e08096115a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927c946c8190aef40679199fede3 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a370a88190b5d17b8a5387138d |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:08 p.m.