Triple
T284539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roman Republic |
E5859
|
entity |
| Predicate | otherOffice |
P9115
|
FINISHED |
| Object | praetor |
—
|
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: praetor | Statement: [Roman Republic, otherOffice, praetor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherOffice Context triple: [Roman Republic, otherOffice, praetor]
-
A.
leftOffice
Indicates that an entity ceased holding or performing the duties of a particular office or position.
-
B.
office
Indicates that an entity holds or occupies an official position, role, or post within an organization or institution.
-
C.
officeInvolved
Indicates that a particular office or organizational unit is involved or participates in a specified event, action, or relationship.
-
D.
limitsOffice
Indicates that one entity imposes a restriction or cap on the scope, duration, or powers of another entity’s office or official position.
-
E.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
- 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_69a25946a7ac8190a78871c210213272 |
completed | Feb. 28, 2026, 2:56 a.m. |
| NER | Named-entity recognition | batch_69a25e2aba74819093eddd8d820260c0 |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b795a6c8190944d48e8418e0ccd |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25c4b773c81908f1017f40b0bfd07 |
completed | Feb. 28, 2026, 3:08 a.m. |
Created at: Feb. 28, 2026, 3:02 a.m.