Triple
T4968427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FEMA Region II |
E111582
|
entity |
| Predicate | numberOfStatesAndTerritoriesCovered |
P14077
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [FEMA Region II, numberOfStatesAndTerritoriesCovered, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStatesAndTerritoriesCovered Context triple: [FEMA Region II, numberOfStatesAndTerritoriesCovered, 4]
-
A.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
B.
numberOfJurisdictions
Indicates the count of distinct legal or administrative jurisdictions associated with or applicable to an entity or situation.
-
C.
numberOfStatesRepresented
chosen
Indicates how many distinct states are represented or covered in a given context or entity.
-
D.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
E.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
- 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_69bd441a0eb481908050fa4273b19eae |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd72e49b048190bac55d9e7a6f7963 |
completed | March 20, 2026, 4:16 p.m. |
| PD | Predicate disambiguation | batch_69bd71447fe88190bb62c5e8753da7a7 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:32 p.m.