Triple
T18888413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Man on Campus |
E462017
|
entity |
| Predicate | locationOfResidenceInStory |
P98527
|
FINISHED |
| Object | university bell tower |
—
|
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: university bell tower | Statement: [Big Man on Campus, locationOfResidenceInStory, university bell tower]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locationOfResidenceInStory Context triple: [Big Man on Campus, locationOfResidenceInStory, university bell tower]
-
A.
homeLocationInStory
chosen
Indicates the place that serves as a character’s primary home or base of residence within the context of the story.
-
B.
stateOfFictionalResidence
Indicates the state or region in which a fictional character’s residence is located.
-
C.
settingOfFictionalResidence
Indicates that a location serves as the setting or backdrop for a fictional residence within a narrative work.
-
D.
residenceAtStartOfFilm
Indicates the place where a person or character is living at the beginning of the film.
-
E.
residencyLocation
Indicates the place where an entity lives or maintains its primary residence.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c478d8c481909291e7c471e5095a |
completed | April 20, 2026, 6:15 a.m. |
| PD | Predicate disambiguation | batch_69e4a2e27e1481908a8da10b28f07875 |
completed | April 19, 2026, 9:39 a.m. |
Created at: April 10, 2026, 11:58 a.m.