Triple
T29146217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Accuracy Shooting |
E738780
|
entity |
| Predicate | typicalTargetsCount |
P9099
|
FINISHED |
| Object | four targets |
—
|
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: four targets | Statement: [Accuracy Shooting, typicalTargetsCount, four targets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTargetsCount Context triple: [Accuracy Shooting, typicalTargetsCount, four targets]
-
A.
numberOfTargets
chosen
Indicates the quantity of target entities associated with or affected by a given subject or event.
-
B.
typicalTargetType
Indicates the usual or most common type or category of entity that serves as the target or recipient in a given relationship or action.
-
C.
typicalTargetIncrement
Indicates the usual or expected amount by which a target value is intended to increase in a given adjustment or period.
-
D.
slotCountTypical
Indicates the usual or standard number of slots associated with an entity under normal conditions.
-
E.
typicalNumberOfComponents
Indicates the usual or standard count of distinct components that an entity is expected to have.
- 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_69f07cb46f148190874eb8576a447567 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_6a0186cde2fc819080d7c06503dcc479 |
completed | May 11, 2026, 7:35 a.m. |
| PD | Predicate disambiguation | batch_6a0183ff6d34819080c8ad7c11611a36 |
completed | May 11, 2026, 7:23 a.m. |
Created at: April 28, 2026, 11:39 a.m.