Triple
T7443982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Military Medal (United Kingdom) |
E171823
|
entity |
| Predicate | awardsTotalApprox |
P3160
|
FINISHED |
| Object | over 115000 awards during the First World War |
—
|
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: over 115000 awards during the First World War | Statement: [Military Medal (United Kingdom), awardsTotalApprox, over 115000 awards during the First World War]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awardsTotalApprox Context triple: [Military Medal (United Kingdom), awardsTotalApprox, over 115000 awards during the First World War]
-
A.
numberOfAwards
chosen
Indicates the total count of awards that have been received by an entity.
-
B.
academyAwardsNominationsCount
Indicates the number of times an entity has been nominated for an Academy Award.
-
C.
typicalAwardAmount
Indicates the usual or most common amount of an award given in this relationship.
-
D.
hasMultipleAwardsIndicatedBy
Indicates that an entity is recognized as having received multiple awards, as evidenced or signaled by a specified source or indicator.
-
E.
relatedAward
Indicates that there is an award connected or associated with the subject entity, such as an honor, prize, or recognition related to it.
- F. None of above.
Provenance (3 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_69c68a65402881908f7869368eb746fb |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f36e9a588190b54b8bae181fc971 |
completed | March 27, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69c6f038582c8190bac77c9b5a34b862 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:13 p.m.