Triple
T36255633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brighton Bullet |
E891927
|
entity |
| Predicate | usesStartingStalls |
P195017
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Brighton Bullet, usesStartingStalls, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesStartingStalls Context triple: [Brighton Bullet, usesStartingStalls, true]
-
A.
hasStalls
Indicates that an entity contains, provides, or is equipped with stalls (e.g., booths, compartments, or vendor stands).
-
B.
hasTypeOfStalls
Indicates that an entity features or includes stalls of a particular type or category.
-
C.
numberOfStalls
Indicates the quantity of stalls associated with or contained within a given entity or location.
-
D.
hasStabling
Indicates that one entity provides or contains stabling facilities (such as stalls or accommodation) for another, typically for animals like horses.
-
E.
hasStablingCapacity
Indicates the maximum number of animals or vehicles that a facility can accommodate for stabling or storage.
- F. None of above. chosen
Provenance (4 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_69f76e4599108190811532e707d6bc2c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd974d75e08190af46b1d608769f3b |
completed | May 8, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69fd94ff792c8190bedf4a639d3da809 |
completed | May 8, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69fd974c0e8481909fdd312897c647b3 |
completed | May 8, 2026, 7:57 a.m. |
Created at: May 3, 2026, 4:09 p.m.