Triple
T317893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ethiopia |
E7746
|
entity |
| Predicate | shortPeriodOccupationYears |
P7934
|
FINISHED |
| Object | 1936–1941 |
—
|
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: 1936–1941 | Statement: [Ethiopia, shortPeriodOccupationYears, 1936–1941]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shortPeriodOccupationYears Context triple: [Ethiopia, shortPeriodOccupationYears, 1936–1941]
-
A.
locationPeriod
chosen
Indicates that an entity is associated with being at a particular location during a specified time period.
-
B.
termLength
Indicates the duration or period of time for which an agreement, position, or condition remains in effect.
-
C.
retirementPeriod
Indicates the time span during which an entity is considered to be retired or in retirement status.
-
D.
periodOfMajorUse
Indicates the time span during which something was primarily or most intensively used.
-
E.
hadOccupationStatusUntil
Indicates that an entity held a particular occupational status up to, but not necessarily beyond, a specified point in time.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eb7df63c8190b7cd1bcfdfd96187 |
completed | Feb. 28, 2026, 1:19 p.m. |
| PD | Predicate disambiguation | batch_69a2e94513ec819089f5177f7a521e65 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.