Triple
T28402142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chess Olympiad |
E719419
|
entity |
| Predicate | boardCount |
P164707
|
FINISHED |
| Object | 4 boards in open section (modern standard) |
—
|
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: 4 boards in open section (modern standard) | Statement: [Chess Olympiad, boardCount, 4 boards in open section (modern standard)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: boardCount Context triple: [Chess Olympiad, boardCount, 4 boards in open section (modern standard)]
-
A.
benchCount
Indicates the number of benches associated with a given entity or location.
-
B.
ballCount
Indicates the number of balls associated with a given entity or context.
-
C.
deckNumber
Indicates the specific deck or floor level associated with an entity within a multi-level structure or vehicle.
-
D.
numberOfPieces
Indicates the quantity of discrete parts or units into which something is divided or composed.
-
E.
tileCount
Indicates the number of tiles associated with or contained by a given entity or area.
- 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_69eff6efd1b08190ae3cefd4f11388a2 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64ee0c2788190a94a04ad1902fd5e |
completed | May 2, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_69f64caede108190a35cc7cbfead866f |
completed | May 2, 2026, 7:12 p.m. |
| PDg | Predicate description generation | batch_69f64e36c57c8190af09470a8d35512b |
completed | May 2, 2026, 7:19 p.m. |
Created at: April 28, 2026, 1:20 a.m.