Triple
T1570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IRE Medal of Honor |
E29
|
entity |
| Predicate | domain |
P87
|
FINISHED |
| Object | science and technology awards |
—
|
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: science and technology awards | Statement: [IRE Medal of Honor, domain, science and technology awards]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: domain Context triple: [IRE Medal of Honor, domain, science and technology awards]
-
A.
website
Indicates that one entity is the official website or web presence associated with another entity.
-
B.
category
chosen
Indicates that one entity is classified as a member or type within the grouping or class defined by another entity.
-
C.
sector
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
D.
memberOf
Indicates that an entity belongs to, is part of, or is a constituent of a larger group, organization, or collection.
-
E.
employer
Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
- 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_69a22a285828819081a58308fb963df1 |
completed | Feb. 27, 2026, 11:35 p.m. |
| NER | Named-entity recognition | batch_69a2316d88a08190b2e03041674b5674 |
completed | Feb. 28, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69a230c3ee4481908216244c38aa8aef |
completed | Feb. 28, 2026, 12:03 a.m. |
Created at: Feb. 27, 2026, 11:36 p.m.