Triple
T6467430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Valentine's Day Massacre |
E142263
|
entity |
| Predicate | numberOfGunmen |
P70974
|
FINISHED |
| Object | at least 4 |
—
|
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: at least 4 | Statement: [Saint Valentine's Day Massacre, numberOfGunmen, at least 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGunmen Context triple: [Saint Valentine's Day Massacre, numberOfGunmen, at least 4]
-
A.
numberOfGuns
Indicates the quantity of guns associated with a given entity or situation.
-
B.
numberOfPeopleShot
Indicates the count of individuals who were shot in a particular event or context.
-
C.
numberOfGunshotWounds
Indicates the count of gunshot wounds associated with a particular entity or event.
-
D.
hasGunfights
Indicates that there are one or more gunfights occurring between the related entities.
-
E.
estimatedMurdersCommitted
Indicates an approximate count of murders that are believed or inferred to have been committed by an entity.
- 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_69c008d3bf4c8190bcf798c5ba9d6fb3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06a12ccf481908f71f888cd744b64 |
completed | March 22, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69c0673d46a08190bc8bcd29f9555fe7 |
completed | March 22, 2026, 10:03 p.m. |
| PDg | Predicate description generation | batch_69c067da970481908a038995ba7dfb4b |
completed | March 22, 2026, 10:06 p.m. |
Created at: March 22, 2026, 4:49 p.m.