Triple
T833045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bloody Friday |
E18009
|
entity |
| Predicate | numberOfInjured |
P661
|
FINISHED |
| Object | over 100 |
—
|
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 100 | Statement: [Bloody Friday, numberOfInjured, over 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInjured Context triple: [Bloody Friday, numberOfInjured, over 100]
-
A.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
B.
causeOfInjury
Indicates that one entity is the source or reason that another entity sustained an injury.
-
C.
numberOfTrainsInvolved
Indicates the count of trains that are involved in a particular event, situation, or incident.
-
D.
estimatedNumberOfPeopleSaved
Indicates the approximate count of individuals whose lives were preserved or harm was averted as a result of a particular action, intervention, or entity.
-
E.
casualtiesEstimate
chosen
Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abb647988190950e1790bcfa60a5 |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7b3d2481909199f7c9f305bdfe |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.