Triple
T20871448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M35A2 |
E513901
|
entity |
| Predicate | usedByOtherCountries |
P142181
|
FINISHED |
| Object | many allied and foreign militaries |
—
|
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: many allied and foreign militaries | Statement: [M35A2, usedByOtherCountries, many allied and foreign militaries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedByOtherCountries Context triple: [M35A2, usedByOtherCountries, many allied and foreign militaries]
-
A.
usedByCountriesWith
Indicates that something (such as an item, system, or practice) is utilized or employed by one or more specified countries in common.
-
B.
usedInCountries
Indicates that something is utilized or applied within one or more specified countries.
-
C.
usedInCountry
Indicates that something is utilized, applied, or in operation within the specified country.
-
D.
usedForCountry
Indicates that something is used for, or serves a purpose related to, a specific country.
-
E.
usedWithCountryName
Indicates that something (such as a term, label, or identifier) is used specifically in conjunction with a country name.
- F. None of above. chosen
Provenance (4 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_69e0b4f675cc8190b4e745225b62eb66 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c4649cf88190b3ad946576aa46aa |
completed | April 21, 2026, 12:27 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a593f481908beb457c29f1ce73 |
completed | April 20, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e5d53c4d6881909b4d0a716fa5ed4a |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 16, 2026, 12:45 p.m.