Triple
T18168277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harry Bailey |
E434951
|
entity |
| Predicate | isWarVeteran |
P65983
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Harry Bailey, isWarVeteran, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isWarVeteran Context triple: [Harry Bailey, isWarVeteran, true]
-
A.
isVeteranOf
Indicates that an entity has previously served in and been discharged from a specified military conflict, campaign, or armed force.
-
B.
hadMilitaryServiceFrom
Indicates that an entity performed or was engaged in military service starting from a specified point in time.
-
C.
hasMilitaryStatus
chosen
Indicates that an entity possesses a specific military affiliation, role, or status (such as active duty, reserve, or veteran).
-
D.
yearsOfMilitaryService
Indicates the number of years an entity has served or is recorded as serving in the military.
-
E.
personReferredToMilitaryService
Indicates that one person has directed, recommended, or assigned another person to perform military service.
- 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_69d8b90b7a188190b3fc7b8d4a6cd20a |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df52f8b08190ab2c4d76b510cd28 |
completed | April 19, 2026, 1:57 p.m. |
| PD | Predicate disambiguation | batch_69e4331baeb88190b21f50a98c36c78e |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:30 a.m.