Triple
T24595713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gimme the Loot |
E608661
|
entity |
| Predicate | containsRobberyReferences |
P156690
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Gimme the Loot, containsRobberyReferences, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsRobberyReferences Context triple: [Gimme the Loot, containsRobberyReferences, true]
-
A.
numberOfKnownMajorRobberies
Indicates the count of significant robbery incidents that are known or recorded for a given entity.
-
B.
collectionBeforeTheft
Indicates that a collection or gathering of items occurred prior to the act of theft.
-
C.
numberOfKnownRobberies
Indicates the count of robbery incidents that are known or recorded as having occurred.
-
D.
plansRobberyIn
Indicates that an entity is preparing or organizing to commit a robbery within a specified location or jurisdiction.
-
E.
casualtiesRobbersKilled
Indicates that the casualties in an incident were individuals who were robbers that were killed.
- F. None of above. chosen
Provenance (4 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_69e2c4cf54248190af7b0c2d9ade9830 |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2be044d4c819094e14eda28d371a7 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6ca751c8190a040c10d701ecf3a |
completed | April 30, 2026, 12:48 a.m. |
| PDg | Predicate description generation | batch_69f2b8b8bc5881908df49c0b07110246 |
completed | April 30, 2026, 2:04 a.m. |
Created at: April 18, 2026, 2:30 a.m.