Triple
T15251446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paço de São Cristóvão |
E364526
|
entity |
| Predicate | fireConsequence |
P52514
|
FINISHED |
| Object | severe damage to building |
—
|
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: severe damage to building | Statement: [Paço de São Cristóvão, fireConsequence, severe damage to building]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fireConsequence Context triple: [Paço de São Cristóvão, fireConsequence, severe damage to building]
-
A.
fireOccurred
Indicates that a fire event took place at a specific time and/or location.
-
B.
fireEffect
chosen
Indicates that one entity produces, causes, or is associated with a fire-related impact or consequence on another entity.
-
C.
fireType
Indicates that one entity has a specific classification or category related to fire (e.g., type, kind, or nature of fire).
-
D.
fires
Indicates that an agent initiates the discharge or ignition of something, such as a weapon, engine, or explosive device, causing it to operate or go off.
-
E.
fireUse
Indicates the use or application of fire by one entity on, with, or for another entity or object.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007f728648190b2c86e4528542b65 |
completed | April 15, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69deca8d1bd48190a4b94f29b425e335 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:13 a.m.