Triple
T7187255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American football |
E167602
|
entity |
| Predicate | safetyPoints |
P75901
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [American football, safetyPoints, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyPoints Context triple: [American football, safetyPoints, 2]
-
A.
safety
Indicates that an entity provides, ensures, or is associated with protection from harm, danger, or risk for another entity or within a given context.
-
B.
safetyConcept
Indicates that something embodies, represents, or is associated with a principle, idea, or framework related to safety.
-
C.
safetyProfile
Indicates the overall level and characteristics of risk or harm associated with something, typically summarizing how safe it is under specified conditions.
-
D.
safetyRelevant
Indicates that the associated entity, condition, or information has a direct impact on safety or is critical for preventing harm or accidents.
-
E.
safetyCategory
Indicates the classification of something according to its level or type of safety.
- 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_69c6888b5248819090499a884ee3ec39 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e9b045c48190b27b2d6f7c11026f |
completed | March 27, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69c6e74fb0f48190b2ad4dd4efdd241a |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e9aeb1b08190ace6f978387c89aa |
completed | March 27, 2026, 8:33 p.m. |
Created at: March 27, 2026, 2:50 p.m.