Triple
T31602798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John DiGiovanni |
E806390
|
entity |
| Predicate | numberOfVictimsInSameAttack |
P154584
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [John DiGiovanni, numberOfVictimsInSameAttack, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVictimsInSameAttack Context triple: [John DiGiovanni, numberOfVictimsInSameAttack, 6]
-
A.
numberOfVictimsInSameEvent
chosen
Indicates the count of distinct victims involved in the same specific event or incident.
-
B.
numberOfVictimsClaimed
Indicates the reported count of victims associated with a particular event, incident, or action.
-
C.
numberOfVictimsTargeted
Indicates the quantity of victims that were intended or selected as targets in the referenced act or event.
-
D.
numberOfVictimsConfirmed
Indicates the confirmed count of victims associated with an event, incident, or situation.
-
E.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
- 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_69f348d54ccc8190a03b5df9a2b40b25 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a01289f781481908f3788f8a719f2f4 |
completed | May 11, 2026, 12:53 a.m. |
| PD | Predicate disambiguation | batch_6a012823c7248190961e20be48dd6246 |
completed | May 11, 2026, 12:51 a.m. |
Created at: April 30, 2026, 10:33 p.m.