Triple
T30114364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1998 FIFA World Cup qualification |
E765371
|
entity |
| Predicate | AFCAllocatedSlots |
P66785
|
FINISHED |
| Object | 3.5 |
—
|
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: 3.5 | Statement: [1998 FIFA World Cup qualification, AFCAllocatedSlots, 3.5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: AFCAllocatedSlots Context triple: [1998 FIFA World Cup qualification, AFCAllocatedSlots, 3.5]
-
A.
numberOfTimeSlotsPerCarrier
Indicates the quantity of discrete time slots that are allocated or assigned to each individual carrier.
-
B.
AFCSpots
Indicates a relationship where specific locations or positions are designated as AFC (Automated/Automatic Fare Collection) spots, typically points where fare-related interactions occur.
-
C.
confederationSlotAllocation
chosen
Indicates how available positions or slots are distributed among different confederations within a larger system or competition.
-
D.
numberOfTimeSlotsPerFrame
Indicates the total count of discrete time slots that are contained within a single frame in a time-structured system or protocol.
-
E.
seatsAllocatedBy
Indicates that seats are assigned or distributed by a particular agent, authority, or mechanism.
- 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_69f22475ad7c8190be7f9541044a0bbb |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67dc294488190af7314b78253404e |
completed | May 2, 2026, 10:42 p.m. |
| PD | Predicate disambiguation | batch_69f673c664f08190b4d66cdc305e10db |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 7:11 p.m.