Triple
T31413121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office of the Chief Information Officer (FDIC) |
E801320
|
entity |
| Predicate | focusArea |
P3
|
FINISHED |
| Object | enterprise data and systems integration |
—
|
LITERAL FINISHED |
How this triple was built (1 step)
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: enterprise data and systems integration | Statement: [Office of the Chief Information Officer (FDIC), focusArea, enterprise data and systems integration]
Provenance (2 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_69f348c0dd648190bf2fd7642f78eb06 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a08e47dc8190a0676a05011d3e69 |
completed | May 3, 2026, 1:10 a.m. |
Created at: April 30, 2026, 8:40 p.m.