Triple
T14707605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexander Mahone |
E345465
|
entity |
| Predicate | professionBeforeFBI |
P115446
|
FINISHED |
| Object | military intelligence officer |
—
|
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: military intelligence officer | Statement: [Alexander Mahone, professionBeforeFBI, military intelligence officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionBeforeFBI Context triple: [Alexander Mahone, professionBeforeFBI, military intelligence officer]
-
A.
professionBeforePolitics
Indicates that a person’s occupation or career occurred prior to their involvement in politics.
-
B.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
-
C.
professionOfCandidate
Indicates that one entity is the profession or occupational role held by the candidate entity.
-
D.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- F. None of above. chosen
Provenance (4 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb609965081908f654bcb9eaaa145 |
completed | April 14, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69de716d3aac8190aaa6dc1f099b86e8 |
completed | April 14, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:28 a.m.