Triple
T20684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Chicago Fire of 1871 |
E410
|
entity |
| Predicate | economicDamage |
P1584
|
FINISHED |
| Object | approximately 200 million US dollars (1871) |
—
|
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: approximately 200 million US dollars (1871) | Statement: [Great Chicago Fire of 1871, economicDamage, approximately 200 million US dollars (1871) ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: economicDamage Context triple: [Great Chicago Fire of 1871, economicDamage, approximately 200 million US dollars (1871) ]
-
A.
damageYear
Indicates the year in which the damage to an entity occurred or was recorded.
-
B.
damagedIn
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
C.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
D.
civilianImpact
Indicates the extent to which an action, event, or situation affects civilians, especially in terms of harm, disruption, or other consequences.
-
E.
economicSystem
Indicates the type or structure of the economic organization or system under which an entity operates or to which it belongs.
- 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_69a240778d288190815c0052ebbbcc91 |
completed | Feb. 28, 2026, 1:10 a.m. |
| NER | Named-entity recognition | batch_69a246f7bd30819085f751c41f6f029e |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a246526f5881909bc2a46e978bd082 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a246f4d7908190a947f6da251c6f3b |
completed | Feb. 28, 2026, 1:37 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.