Triple
T21063018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vel d’Hiv Roundup |
E518897
|
entity |
| Predicate | ageOfOldestVictims |
P70790
|
FINISHED |
| Object | elderly |
—
|
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: elderly | Statement: [Vel d’Hiv Roundup, ageOfOldestVictims, elderly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageOfOldestVictims Context triple: [Vel d’Hiv Roundup, ageOfOldestVictims, elderly]
-
A.
oldestVictimAge
chosen
Indicates the age of the oldest individual among the victims involved in the event or situation.
-
B.
victimAge
Indicates the age of the person who is the victim in the described event or situation.
-
C.
numberOfAdultVictims
Indicates the count of adult individuals who are victims in the described event or situation.
-
D.
numberOfChildVictims
Indicates the count of individuals who are victims and are classified as children in the context of the described event or situation.
-
E.
numberOfAdultVictimsKilled
Indicates the total count of adult victims who were killed in the described event or incident.
- 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.