Triple
T11467851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 4×100 metre freestyle relay |
E271822
|
entity |
| Predicate | subsequentLegStart |
P99444
|
FINISHED |
| Object | relay takeoff from poolside |
—
|
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: relay takeoff from poolside | Statement: [4×100 metre freestyle relay, subsequentLegStart, relay takeoff from poolside]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subsequentLegStart Context triple: [4×100 metre freestyle relay, subsequentLegStart, relay takeoff from poolside]
-
A.
subsequentPosition
Indicates that one entity occupies a position that comes directly after another entity in a defined order or sequence.
-
B.
secondLegOrigin
Indicates the location from which the second leg of a multi-leg journey or route begins.
-
C.
firstLeg
Indicates that one entity represents the initial segment or starting portion of a multi-part journey, sequence, or process involving another entity.
-
D.
subsequentLandRuns
Indicates that one land run event occurred after another in temporal sequence.
-
E.
subsequentCase
Indicates that one legal case follows another in time or procedural order, often relying on or referencing the earlier case.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d82949e3688190b024a4980666c94f |
completed | April 9, 2026, 10:33 p.m. |
| PD | Predicate disambiguation | batch_69d8086ecd6c81908f424864857762d6 |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d8279925e4819089210611c0d8e61a |
completed | April 9, 2026, 10:26 p.m. |
Created at: April 8, 2026, 9:35 p.m.