Triple
T414789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1972 Summer Olympics |
E9567
|
entity |
| Predicate | hostNationTotalMedals |
P14576
|
FINISHED |
| Object | 40 |
—
|
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: 40 | Statement: [1972 Summer Olympics, hostNationTotalMedals, 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hostNationTotalMedals Context triple: [1972 Summer Olympics, hostNationTotalMedals, 40]
-
A.
hostNationMedalRank
Indicates the ranking position of the host nation in the overall medal standings for a sporting event or multi-sport competition.
-
B.
hostCountryMedalRank
Indicates the ranking position of the host country in the overall medal standings for a given sporting event or competition.
-
C.
hostNationCapital
Indicates that a city serves as the capital of a particular host nation.
-
D.
topMedalCountry
Indicates that a country is the one with the highest total medal count (or ranking) in a given competition or event.
-
E.
USMedalsTotal
Indicates the total number of medals won by the United States in a given competition or event.
- 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edcff4688190809d83d112ff25a5 |
completed | Feb. 28, 2026, 1:29 p.m. |
| PDg | Predicate description generation | batch_69a2eeb8545c8190a2b8517e7ed5b92e |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:09 p.m.