Triple
T284544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roman Republic |
E5859
|
entity |
| Predicate | emergencyOffice |
P9116
|
FINISHED |
| Object | dictator |
—
|
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: dictator | Statement: [Roman Republic, emergencyOffice, dictator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emergencyOffice Context triple: [Roman Republic, emergencyOffice, dictator]
-
A.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
-
B.
servesAsPrimaryTeachingHospitalFor
Indicates that one institution functions as the main clinical training and teaching site for another institution, typically a medical school or academic program.
-
C.
hasEmergencyIntercoms
Indicates that an entity is equipped with emergency intercom devices available for use in urgent or crisis situations.
-
D.
policePrecinct
Indicates that a specified location, building, or area functions as or is designated as a police precinct.
-
E.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
- 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.