Triple
T21063015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vel d’Hiv Roundup |
E518897
|
entity |
| Predicate | numberOfChildrenArrested |
P130530
|
FINISHED |
| Object | over 4000 |
—
|
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: over 4000 | Statement: [Vel d’Hiv Roundup, numberOfChildrenArrested, over 4000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfChildrenArrested Context triple: [Vel d’Hiv Roundup, numberOfChildrenArrested, over 4000]
-
A.
numberOfArrests
Indicates the count of times an entity has been arrested.
-
B.
membersArrestedIn
Indicates that certain members of a group or organization were arrested in a specified location or during a particular event or operation.
-
C.
numberOfChildVictims
chosen
Indicates the count of individuals who are victims and are classified as children in the context of the described event or situation.
-
D.
numberOfChildrenMurdered
Indicates the count of children who have been killed in an act of murder.
-
E.
wasArrested
Indicates that an authority detained and took a person into legal custody in connection with a suspected offense.
- 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_69e0b505ef108190b25dd4033e2ff7eb |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6feb15698819090246698b143cb56 |
completed | April 21, 2026, 4:36 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf9d71881908cd85dfc37db93ca |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:39 p.m.