Triple
T20683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Chicago Fire of 1871 |
E410
|
entity |
| Predicate | peopleLeftHomeless |
P1405
|
FINISHED |
| Object | more than 100,000 |
—
|
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: more than 100,000 | Statement: [Great Chicago Fire of 1871, peopleLeftHomeless, more than 100,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peopleLeftHomeless Context triple: [Great Chicago Fire of 1871, peopleLeftHomeless, more than 100,000]
-
A.
civilianDisplacement
Indicates the forced or compelled movement of civilian populations from their homes or usual places of residence, typically due to conflict, violence, or persecution.
-
B.
residence
Indicates that one entity lives at, is based in, or habitually occupies the location represented by the other entity.
-
C.
population
Indicates the total number of individuals living in or present within a specified area or group.
-
D.
civilianImpact
chosen
Indicates the extent to which an action, event, or situation affects civilians, especially in terms of harm, disruption, or other consequences.
-
E.
lostTo
Indicates that one entity was defeated by another in a competition, conflict, or comparison.
- 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_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. |
Created at: Feb. 28, 2026, 1:14 a.m.