Triple
T34550187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Course 1st tee |
E887039
|
entity |
| Predicate | fairwayWidthReputation |
P179310
|
FINISHED |
| Object | very wide |
—
|
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: very wide | Statement: [Old Course 1st tee, fairwayWidthReputation, very wide]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fairwayWidthReputation Context triple: [Old Course 1st tee, fairwayWidthReputation, very wide]
-
A.
fairwayType
Indicates the specific kind or classification of a fairway associated with an entity.
-
B.
fairwayGrassType
Indicates the type or variety of grass used on a golf course fairway.
-
C.
hasYardage
Indicates that something possesses or is associated with a specific measured distance or length, typically expressed in yards.
-
D.
golfCourseLength
Indicates the measured distance or extent of a golf course, typically from tee to hole or across the full course layout.
-
E.
hasWaterHazards
Indicates that the subject contains or is associated with one or more water-based obstacles or danger areas.
- 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_69f349cff89081908f91e0b064f4833e |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f72024a3348190b5e21ba600e64bb4 |
completed | May 3, 2026, 10:15 a.m. |
| PD | Predicate disambiguation | batch_69f71cc8074c81909ae09bea2acf1a09 |
completed | May 3, 2026, 10 a.m. |
| PDg | Predicate description generation | batch_69f71f8df5d48190944fbfbd9d573868 |
completed | May 3, 2026, 10:12 a.m. |
Created at: May 1, 2026, 2:02 a.m.