Triple
T4186788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Director of the Cybersecurity and Infrastructure Security Agency |
E88330
|
entity |
| Predicate | sectoralScope |
P52507
|
FINISHED |
| Object | communications sector |
—
|
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: communications sector | Statement: [Director of the Cybersecurity and Infrastructure Security Agency, sectoralScope, communications sector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectoralScope Context triple: [Director of the Cybersecurity and Infrastructure Security Agency, sectoralScope, communications sector]
-
A.
sector
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
B.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
C.
sectorInfluence
Indicates the degree to which one sector affects, shapes, or exerts control over another sector or over outcomes within that sector.
-
D.
targetsSector
chosen
Indicates that an entity is directed toward, focused on, or intended to affect a particular economic or industry sector.
-
E.
notableSector
Indicates that an entity is particularly prominent, influential, or significant within a specified sector or industry.
- 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_69aed9477e8c81908bcb862d2db55b1d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af04b009dc8190abda3f149a5b16fa |
completed | March 9, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69af01935064819096b7619f42e164dd |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:45 p.m.