Triple
T6512228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mo Farah |
E150162
|
entity |
| Predicate | europeanIndoorChampionIn |
P71305
|
FINISHED |
| Object | 3000 metres |
—
|
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: 3000 metres | Statement: [Mo Farah, europeanIndoorChampionIn, 3000 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: europeanIndoorChampionIn Context triple: [Mo Farah, europeanIndoorChampionIn, 3000 metres]
-
A.
wonEuropeanChampionship
Indicates that an entity achieved first place or overall victory in a European Championship competition.
-
B.
wonEuropeanTrophy
Indicates that the subject has achieved victory in a European-level competition or trophy.
-
C.
EuropeanChampionshipBronzeMedals
Indicates that an entity has won one or more bronze medals at a European Championship competition.
-
D.
EuropeanChampionshipTitles
Indicates the number of European Championship titles an entity has won.
-
E.
EuropeanTrophyCount
Indicates the number of European football trophies a team has won in officially recognized continental competitions.
- 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_69c687ef291081909d437f035eef1cda |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c69f3c5eb88190a56723acd8096dd8 |
completed | March 27, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_69c68ab98c78819081743e614df04e1d |
completed | March 27, 2026, 1:48 p.m. |
| PDg | Predicate description generation | batch_69c69f362ee4819090e8fa48caef7d7d |
completed | March 27, 2026, 3:16 p.m. |
Created at: March 27, 2026, 1:44 p.m.