Triple
T30686291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oklahoma City–Fort Worth |
E781193
|
entity |
| Predicate | hasIntermediateStopCity |
P24279
|
FINISHED |
| Object | Norman, Oklahoma |
—
|
NE NERFINISHED |
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: Norman, Oklahoma | Statement: [Oklahoma City–Fort Worth, hasIntermediateStopCity, Norman, Oklahoma]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIntermediateStopCity Context triple: [Oklahoma City–Fort Worth, hasIntermediateStopCity, Norman, Oklahoma]
-
A.
hasIntermediateCity
chosen
Indicates that there is a city located between two other places along a route or connection.
-
B.
hasIntermediateStation
Indicates that a route, journey, or connection includes a station that lies between its starting point and its final destination.
-
C.
numberOfIntermediateStops
Indicates the count of stops or pauses that occur between the starting point and the final destination in a journey or process.
-
D.
hasStops
Indicates that a route, service, or journey includes one or more intermediate stopping points at specified locations.
-
E.
hasNumberOfStops
Indicates the relationship specifying how many stops are associated with a given route, trip, or journey.
- 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_69f224a92f54819095499b4d32bd5134 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69ff0b6bc4a88190bf1d38c6ea26bcdc |
completed | May 9, 2026, 10:24 a.m. |
| PD | Predicate disambiguation | batch_69ff082a22f4819095ded971dbd8ea7b |
completed | May 9, 2026, 10:10 a.m. |
Created at: April 29, 2026, 8:33 p.m.