Triple
T7174577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Gun |
E167286
|
entity |
| Predicate | psychologicalEffect |
P52747
|
FINISHED |
| Object | terror among Parisian population |
—
|
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: terror among Parisian population | Statement: [Paris Gun, psychologicalEffect, terror among Parisian population]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: psychologicalEffect Context triple: [Paris Gun, psychologicalEffect, terror among Parisian population]
-
A.
emotionEffect
chosen
Indicates that one entity’s emotional state causes or influences a change in another entity’s feelings, behavior, or condition.
-
B.
predictedEffect
Indicates that one entity is expected to cause, influence, or result in a particular outcome or consequence for another entity.
-
C.
primaryEffect
Indicates the main direct outcome or consequence that results from a given cause, action, or condition.
-
D.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
-
E.
viewOnPsychology
Indicates that one entity holds a particular perspective, opinion, or theoretical stance regarding the field or subject of psychology.
- 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_69c68889a2748190a316c5e65360361a |
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. |
Created at: March 27, 2026, 2:48 p.m.