Triple
T3950317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cocoa House |
E84848
|
entity |
| Predicate | eventEffect |
P53074
|
FINISHED |
| Object | fire in 1985 damaged upper floors |
—
|
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: fire in 1985 damaged upper floors | Statement: [Cocoa House, eventEffect, fire in 1985 damaged upper floors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eventEffect Context triple: [Cocoa House, eventEffect, fire in 1985 damaged upper floors]
-
A.
eventInfluencedBy
Indicates that an event occurs or unfolds in a way that is causally or significantly affected by another entity, factor, or prior event.
-
B.
emotionEffect
Indicates that one entity’s emotional state causes or influences a change in another entity’s feelings, behavior, or condition.
-
C.
sideEffect
Indicates that one entity is an unintended or secondary effect resulting from the use or occurrence of another entity.
-
D.
impactEvent
Indicates that one entity physically strikes or collides with another, producing a resulting effect or change.
-
E.
notableEffect
Indicates that one entity has a significant impact, consequence, or influence on another entity or situation.
- 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_69aed934fbfc8190847068e4546de963 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaa5afdc8190b709af2473d75d02 |
completed | March 9, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69aef8ed04e4819096bced8971cd888d |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefaa3c6a08190bfe76629c7c98eea |
completed | March 9, 2026, 4:51 p.m. |
Created at: March 9, 2026, 3:30 p.m.