Triple
T34324014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amulet |
E880817
|
entity |
| Predicate | settingPlaceType |
P104439
|
FINISHED |
| Object | university bathroom |
—
|
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 bathroom | Statement: [Amulet, settingPlaceType, university bathroom]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingPlaceType Context triple: [Amulet, settingPlaceType, university bathroom]
-
A.
placeType
Indicates the type or category of place associated with an entity (e.g., city, park, building).
-
B.
settingLocationType
chosen
Indicates the type or category of location in which an event, action, or situation takes place.
-
C.
targetedPlace
Indicates that an entity intentionally directed an action, operation, or influence toward a specific place or location.
-
D.
connectsTypeOfPlace
Indicates a relationship where one place is linked or associated with another based on its type or category of location.
-
E.
places
Indicates that one entity assigns, positions, or puts another entity into a specific location, role, or context.
- 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_69f349b9cd508190a996a616903b3e6d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fd0d0ba5c48190bddb3f0e6637544c |
completed | May 7, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69fd0c4324a8819086c90adf46216e0e |
completed | May 7, 2026, 10:03 p.m. |
Created at: May 1, 2026, 1:58 a.m.