Triple
T149834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Troubles |
E3406
|
entity |
| Predicate | estimatedNumberOfInjured |
P661
|
FINISHED |
| Object | tens of thousands injured |
—
|
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: tens of thousands injured | Statement: [The Troubles, estimatedNumberOfInjured, tens of thousands injured]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedNumberOfInjured Context triple: [The Troubles, estimatedNumberOfInjured, tens of thousands injured]
-
A.
casualtiesEstimate
chosen
Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an event or incident.
-
B.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
C.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
D.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
E.
deathTollEstimate
Indicates an estimated number of deaths attributed to a particular event, cause, or period.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a2580ca15481909fa3e87d804a1b23 |
completed | Feb. 28, 2026, 2:50 a.m. |
| PD | Predicate disambiguation | batch_69a256599db08190a7b000b381d32ec4 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.