Triple
T10820174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Major-General |
E255344
|
entity |
| Predicate | USClosestEquivalent |
P58587
|
FINISHED |
| Object | Major General |
—
|
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: Major General | Statement: [Major-General, USClosestEquivalent, Major General]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: USClosestEquivalent Context triple: [Major-General, USClosestEquivalent, Major General]
-
A.
equivalentIn
chosen
Indicates that two entities are considered logically or functionally the same in meaning, status, or effect within a given context.
-
B.
hasNearbyUSCity
Indicates that one location has at least one city in the United States situated within a specified nearby distance.
-
C.
closestTo
Indicates that one entity is nearer in distance to a reference entity than any other comparable entity.
-
D.
languageEquivalent
Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
-
E.
closerTo
Indicates that one entity is at a smaller distance to a reference entity than another entity is.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73449eee88190afa52c4e6ef96baa |
completed | April 9, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_69d70d1bf3648190b36fa96ea018e0dc |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:18 p.m.