Triple
T27501929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medal of Honor: Airborne score |
E694176
|
entity |
| Predicate | languageOfGame |
P193182
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Medal of Honor: Airborne score, languageOfGame, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfGame Context triple: [Medal of Honor: Airborne score, languageOfGame, English]
-
A.
languageOfTheme
Indicates that a particular language is used to express, describe, or label a given theme or subject.
-
B.
governingLanguage
Indicates the language that holds official or authoritative status over a given entity, such as a region, organization, or document.
-
C.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
D.
languageOfCompetition
Indicates the language in which a competition is conducted or officially presented.
-
E.
testLanguage
Indicates that an entity uses or is associated with a particular language for testing or evaluation purposes.
- 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_69ef538370888190b1ddf53bb4831188 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69fd3a69f1e08190a11aed015bff0858 |
completed | May 8, 2026, 1:20 a.m. |
| PD | Predicate disambiguation | batch_69fd39124180819080ca7911d3515d6d |
completed | May 8, 2026, 1:14 a.m. |
| PDg | Predicate description generation | batch_69fd3a6905b88190ae12b43576f4cc63 |
completed | May 8, 2026, 1:20 a.m. |
Created at: April 27, 2026, 1:11 p.m.