Triple
T17269267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jen Easterly |
E419209
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jen Easterly |
E419209
|
NE 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: Jen Easterly | Statement: [Jen Easterly, name, Jen Easterly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jen Easterly Context triple: [Jen Easterly, name, Jen Easterly]
-
A.
Jen Easterly
chosen
Jen Easterly is a U.S. cybersecurity expert and former intelligence and military officer who leads national efforts to protect critical infrastructure and defend against cyber threats.
-
B.
Rosa Brooks
Rosa Brooks is an American law professor, author, and former Pentagon official known for her work on national security, international law, and civil-military relations.
-
C.
Anne Neuberger
Anne Neuberger is an American national security official known for her leadership in U.S. cybersecurity and technology policy at the highest levels of government.
-
D.
Susan Harrison Berger
Susan Harrison Berger is known as the wife of the late Sandy Berger, who served as U.S. National Security Advisor under President Bill Clinton.
-
E.
Margaret Sixel
Margaret Sixel is an Academy Award–winning film editor best known for her dynamic, high-intensity work on action films such as Mad Max: Fury Road.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d886da626481908a14ce7830329a35 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42f4803b48190894b3bb9a4970602 |
completed | April 19, 2026, 1:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01794a09b8819086da30f38c6d4c20 |
completed | May 11, 2026, 6:38 a.m. |
Created at: April 10, 2026, 5:40 a.m.