Triple
T5236934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Badshah Khan |
E118242
|
entity |
| Predicate | numberOfYearsImprisoned |
P8398
|
FINISHED |
| Object | approximately 30 |
—
|
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: approximately 30 | Statement: [Badshah Khan, numberOfYearsImprisoned, approximately 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfYearsImprisoned Context triple: [Badshah Khan, numberOfYearsImprisoned, approximately 30]
-
A.
imprisonmentYear
Indicates the specific year in which an entity was imprisoned or placed into custody.
-
B.
durationOfImprisonment
chosen
Indicates the length of time that an entity is or was held in imprisonment.
-
C.
imprisonedFor
Indicates that one entity is held in detention or jail as a consequence of, or in connection with, a specific reason, action, or offense committed by another entity or itself.
-
D.
servedPrisonTime
Indicates that an entity has spent a period of time incarcerated in prison as a consequence of a legal sentence.
-
E.
hasBeenImprisonedBy
Indicates that one entity has been confined or incarcerated under the authority or control of another entity.
- 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_69bd4467db0881909b3b0982df32cc8f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b2595c88190b4ca0b99c2f31472 |
completed | March 20, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69bd77c1397c8190a7fd844d7a396e54 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:49 p.m.