Triple
T13088250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malév Hungarian Airlines |
E310391
|
entity |
| Predicate | operationalLifespan |
P13716
|
FINISHED |
| Object | 1946–2012 |
—
|
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: 1946–2012 | Statement: [Malév Hungarian Airlines, operationalLifespan, 1946–2012]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operationalLifespan Context triple: [Malév Hungarian Airlines, operationalLifespan, 1946–2012]
-
A.
durationOfUse
chosen
Indicates the length of time for which something is used or remains in use.
-
B.
hasMeanLifetime
Indicates the characteristic average time duration for which an entity, state, or condition persists before it decays, ends, or changes.
-
C.
operatedSince
Indicates that an entity has been in operation continuously from a specified starting time or date.
-
D.
yearOfUse
Indicates the specific year during which something was in use or actively utilized.
-
E.
operatingMonthsApproximate
Indicates that the time period during which something operates is specified in approximate months rather than exact dates.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d981378dd08190b4f00e4e5df0e480 |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d9803f6c508190bfadfbc2d00c2c64 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:02 p.m.