Triple
T3106146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prosecutor v. Thomas Lubanga Dyilo |
E64834
|
entity |
| Predicate | timePeriodOfCrimes |
P302
|
FINISHED |
| Object | 2002-09-01/2003-08-13 |
—
|
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: 2002-09-01/2003-08-13 | Statement: [Prosecutor v. Thomas Lubanga Dyilo, timePeriodOfCrimes, 2002-09-01/2003-08-13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timePeriodOfCrimes Context triple: [Prosecutor v. Thomas Lubanga Dyilo, timePeriodOfCrimes, 2002-09-01/2003-08-13]
-
A.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
B.
timePeriod
chosen
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
C.
peakViolencePeriod
Indicates the time period during which violence reaches its highest intensity or frequency within a given context.
-
D.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
E.
numberOfArrests
Indicates the count of times an entity has been arrested.
- 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_69ad857eeaf48190b34ebfdaa7a264cf |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada29beff08190b6e1eb6b0608d0eb |
completed | March 8, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69ad9df25d4c81908ff0f6cff55d0563 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:04 p.m.