Triple
T5914229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cuts |
E131537
|
entity |
| Predicate | locationOfFictionalEvent |
P18263
|
FINISHED |
| Object | barbershop in Baltimore |
—
|
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: barbershop in Baltimore | Statement: [Cuts, locationOfFictionalEvent, barbershop in Baltimore]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locationOfFictionalEvent Context triple: [Cuts, locationOfFictionalEvent, barbershop in Baltimore]
-
A.
locatedNearFiction
Indicates that one fictional entity or place is situated close to another within an imagined or narrative context.
-
B.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
-
C.
locationOfEventDescribed
Indicates that one entity is the place or setting where the event described by another entity occurs.
-
D.
setInFictionalLocation
chosen
Indicates that an event, story, or narrative takes place within a fictional or imagined location rather than a real-world setting.
-
E.
cityOfFictionalActivity
Indicates that a fictional activity, event, or storyline takes place in the specified city.
- 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c048fc112c8190b905bf561c9de096 |
completed | March 22, 2026, 7:54 p.m. |
| PD | Predicate disambiguation | batch_69c03352208c8190968efed05a9fd416 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:59 p.m.