Triple
T7249124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt Dillon |
E156550
|
entity |
| Predicate | occupation |
P3
|
FINISHED |
| Object | U.S. Marshal |
E285566
|
NE 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: U.S. Marshal | Statement: [Matt Dillon, occupation, U.S. Marshal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: U.S. Marshal Context triple: [Matt Dillon, occupation, U.S. Marshal]
-
A.
U.S. Marshal
chosen
A U.S. Marshal is a federal law enforcement officer responsible for duties such as protecting the federal judiciary, transporting prisoners, and apprehending fugitives.
-
B.
Sheriff
The Sheriff is the elected chief law enforcement official of a county, responsible for overseeing public safety, managing the sheriff’s office, and operating the county jail.
-
C.
Sherriff’s Deputy
Sherriff’s Deputy is a Thoroughbred racehorse known for being sired by the stallion Curlin.
-
D.
Sherif
Sherif is a masculine given name of Arabic origin commonly used in Egypt and other Arabic-speaking countries.
-
E.
the Officer
The Officer is a zealous and authoritarian figure in Franz Kafka’s "In the Penal Colony," devoted to an elaborate execution machine and the harsh justice system it represents.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c68827b5e481908dc05e145b2c92d4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea77a3588190accf31860170f052 |
completed | March 27, 2026, 8:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7d3a2ba748190890c2980ca4a8ecd |
completed | March 28, 2026, 1:12 p.m. |
Created at: March 27, 2026, 2:56 p.m.