Triple
T691838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regions Field |
E13808
|
entity |
| Predicate | hasOutfieldFeature |
P16726
|
FINISHED |
| Object | berm seating in left-center field |
—
|
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: berm seating in left-center field | Statement: [Regions Field, hasOutfieldFeature, berm seating in left-center field]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOutfieldFeature Context triple: [Regions Field, hasOutfieldFeature, berm seating in left-center field]
-
A.
hasFeature
Indicates that an entity possesses, exhibits, or includes a particular characteristic, attribute, or component.
-
B.
isFeatureLength
Indicates that something (typically a film or video) has a duration long enough to be considered a full-length, standard feature.
-
C.
hasOutfieldWallCovering
Indicates that an outfield wall is covered or surfaced with a particular material or type of covering.
-
D.
hasInteriorFeature
Indicates that an entity contains or includes a specific feature within its interior space.
-
E.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0aebde88190a49d421477713103 |
completed | March 1, 2026, 8:25 p.m. |
| PD | Predicate disambiguation | batch_69a49d221d38819083c0adda81f59b07 |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a49dc20880819085fa60dc1851f9dc |
completed | March 1, 2026, 8:12 p.m. |
Created at: March 1, 2026, 7:36 p.m.