Triple
T16608439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lahore Darbar |
E403503
|
entity |
| Predicate | hadMilitaryFunction |
P5004
|
FINISHED |
| Object | oversight of the Khalsa army |
—
|
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: oversight of the Khalsa army | Statement: [Lahore Darbar, hadMilitaryFunction, oversight of the Khalsa army]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadMilitaryFunction Context triple: [Lahore Darbar, hadMilitaryFunction, oversight of the Khalsa army]
-
A.
hadMilitaryPost
Indicates that an entity held an official position or assignment within a military organization.
-
B.
militaryFunction
chosen
Indicates a relationship where an entity serves a specific role, duty, or operational purpose within a military context.
-
C.
hadMilitaryObligationsTo
Indicates that one party was bound by duty or law to provide military service, support, or protection to another party.
-
D.
hasMilitaryType
Indicates that an entity is associated with or classified under a specific military category, role, or type.
-
E.
militaryBackground
Indicates that an entity has prior or current experience, service, or training in a military organization.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e36093901881909b85af47f75879a9 |
completed | April 18, 2026, 10:44 a.m. |
| PD | Predicate disambiguation | batch_69e296aabc508190b3836a91b49113ad |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:17 a.m.