Triple
T159929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Overton, Pennsylvania |
E3259
|
entity |
| Predicate | offersTours |
P6209
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [West Overton, Pennsylvania, offersTours, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersTours Context triple: [West Overton, Pennsylvania, offersTours, yes]
-
A.
offersActivity
Indicates that one entity provides or makes available a specific activity for another entity to participate in or use.
-
B.
offersProgram
Indicates that an entity provides or makes available a specific program (such as a course, curriculum, or initiative).
-
C.
offersProgramsIn
Indicates that an institution or provider makes educational or training programs available in a particular field, subject, or area.
-
D.
offersViewOf
Indicates that one entity provides a vantage point from which another entity can be seen or visually appreciated.
-
E.
offersDiscipline
Indicates that one entity provides or makes available a particular field of study, training, or area of specialization to another entity.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25855baf48190a1b63f2e5865d957 |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256623704819089d9eeefe05858ce |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a2578329d08190be82e004b8224d2b |
completed | Feb. 28, 2026, 2:48 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.