Triple
T192010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago waterworks |
E3740
|
entity |
| Predicate | imageSubjectOf |
P450
|
FINISHED |
| Object | historic photographs of Great Chicago Fire aftermath |
—
|
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: historic photographs of Great Chicago Fire aftermath | Statement: [Chicago waterworks, imageSubjectOf, historic photographs of Great Chicago Fire aftermath]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: imageSubjectOf Context triple: [Chicago waterworks, imageSubjectOf, historic photographs of Great Chicago Fire aftermath]
-
A.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
B.
capturedBy
Indicates that one entity has been seized, taken into control, or otherwise apprehended by another entity.
-
C.
depictsPerson
Indicates that one entity visually represents or portrays a specific person.
-
D.
isAssociatedWith
Indicates that there exists a connection, relationship, or involvement between two entities without specifying its exact nature.
-
E.
subjectCanBe
Indicates that the subject has the potential or capability to assume, become, or be classified as the specified object or state.
- 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a259669ba08190a5be1d2e10e70b27 |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a2567567508190b3a41329a15c7156 |
completed | Feb. 28, 2026, 2:44 a.m. |
Created at: Feb. 28, 2026, 2:41 a.m.