Triple
T33593775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Need for Speed: High Stakes |
E860501
|
entity |
| Predicate | damageModel |
P176945
|
FINISHED |
| Object | visual damage |
—
|
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: visual damage | Statement: [Need for Speed: High Stakes, damageModel, visual damage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageModel Context triple: [Need for Speed: High Stakes, damageModel, visual damage]
-
A.
damageOccurred
Indicates that some form of harm, loss, or deterioration has taken place as a result of an event or action.
-
B.
damageScope
Indicates the extent or range of harm or impairment caused by an event, action, or condition.
-
C.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
D.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
-
E.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another 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_69f3497e70e48190951c94d072879bec |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f70b0ca081908b24a98937e6ef66 |
completed | May 3, 2026, 7:19 a.m. |
Created at: May 1, 2026, 1:41 a.m.