Triple
T2019699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Directorate of Tourism and Antiquities Police |
E44075
|
entity |
| Predicate | typeOfCrimeHandled |
P7957
|
FINISHED |
| Object | theft of antiquities |
—
|
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: theft of antiquities | Statement: [General Directorate of Tourism and Antiquities Police, typeOfCrimeHandled, theft of antiquities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCrimeHandled Context triple: [General Directorate of Tourism and Antiquities Police, typeOfCrimeHandled, theft of antiquities]
-
A.
typeOfCasesHandled
Indicates the categories or kinds of cases that an entity (such as a person, organization, or system) is responsible for managing or processing.
-
B.
crimeType
chosen
Indicates the specific category or nature of the crime associated with an event or entity.
-
C.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
D.
numberOfArrests
Indicates the count of times an entity has been arrested.
-
E.
typeOfLawEnforcement
Indicates that one entity is a specific kind or category of law enforcement associated with 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8cfa5c88190b55bce5db968665b |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb7a389408190a84a54856352f15b |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:38 p.m.