Triple
T2194420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Order of Bogdan Khmelnitsky |
E49937
|
entity |
| Predicate | firstClassAwardedTo |
P36688
|
FINISHED |
| Object | front and army commanders |
—
|
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: front and army commanders | Statement: [Order of Bogdan Khmelnitsky, firstClassAwardedTo, front and army commanders]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstClassAwardedTo Context triple: [Order of Bogdan Khmelnitsky, firstClassAwardedTo, front and army commanders]
-
A.
firstAwarded
Indicates the time or occasion when an award, honor, or recognition was given for the very first time.
-
B.
firstWinnerYear
Indicates the year in which an entity first won a particular competition, award, or title.
-
C.
yearHonored
Indicates the specific year in which an entity received an honor, award, or formal recognition.
-
D.
hasLaureate
Indicates that an entity (such as an award or prize) has a specific person or group as its laureate or recipient.
-
E.
hasAwardNamedAfter
Indicates that an entity has an award that is named in honor of another entity.
- 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_69a88aaba3c48190b351cab9b26989ff |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbf74147c81908793c3694894f94a |
completed | March 7, 2026, 6:02 a.m. |
| PD | Predicate disambiguation | batch_69abbda52328819089c7ab111bebb0ca |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbea8bd4881908f72019a5acf6174 |
completed | March 7, 2026, 5:59 a.m. |
Created at: March 4, 2026, 7:46 p.m.