Triple
T2594362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thrilla in Manila |
E58193
|
entity |
| Predicate | stoppageType |
P25018
|
FINISHED |
| Object | corner retirement |
—
|
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: corner retirement | Statement: [Thrilla in Manila, stoppageType, corner retirement]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stoppageType Context triple: [Thrilla in Manila, stoppageType, corner retirement]
-
A.
hasStopType
chosen
Indicates that a stop or stopping point is classified as having a particular type or category of stop.
-
B.
typicalStallType
Indicates the usual or most common type or category of stall associated with an entity.
-
C.
stoppedAt
Indicates that an entity has come to a halt or pause at a specific location or point in time.
-
D.
majorStop
Indicates that a location functions as a primary or significant stop along a route or service path, typically where vehicles regularly halt for boarding, alighting, or key operations.
-
E.
stationType
Indicates the specific category or classification of a station based on its function, services, or operational characteristics.
- 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_69ab4ac14040819098b13f4a27d5c8ff |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd427f58c8190af1c1a9724158c96 |
completed | March 7, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69abd0d344988190a18dd93b13e002e6 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:49 p.m.