Triple
T3279980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cupid |
E68848
|
entity |
| Predicate | effectOfLeadArrows |
P47679
|
FINISHED |
| Object | arouse aversion |
—
|
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: arouse aversion | Statement: [Cupid, effectOfLeadArrows, arouse aversion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOfLeadArrows Context triple: [Cupid, effectOfLeadArrows, arouse aversion]
-
A.
effectOfDeath
Indicates the causal impact or consequences that a death has on another entity, state, or process.
-
B.
involvedPhysicalEffect
Indicates that one entity participates in causing, experiencing, or mediating a physical effect on another entity or the environment.
-
C.
numberOfShotsFired
Indicates the total count of shots that were discharged in the described event or action.
-
D.
weaponUsedAgainst
Indicates that a particular weapon or instrument is employed in an act of aggression, attack, or harm directed toward a specific target or entity.
-
E.
placeOfEffect
Indicates the location or setting where an action, event, or effect takes place or is realized.
- 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_69ad859c463481909ca4be267336c290 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0166c8c81909cb0a580ef319be3 |
completed | March 8, 2026, 5:21 p.m. |
| PD | Predicate disambiguation | batch_69ada420167c81909b6e2702db296d9e |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada526764881908e4bd52938d5374d |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:10 p.m.