Triple
T9622664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gia Lâm Airport |
E232381
|
entity |
| Predicate | hasICAOCode |
P419
|
FINISHED |
| Object |
VVGL
VVGL is the ICAO airport code assigned to Gia Lâm Airport in Hanoi, Vietnam.
|
E809817
|
NE FINISHED |
How this triple was built (4 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: VVGL | Statement: [Gia Lâm Airport, hasICAOCode, VVGL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VVGL Context triple: [Gia Lâm Airport, hasICAOCode, VVGL]
-
A.
VGF
VGF is the municipal public transport operator responsible for running Frankfurt am Main’s urban transit network, including its U-Bahn and tram services.
-
B.
WGL
WGL is the common abbreviation for the Leibniz Association, a major German network of non-university research institutes spanning a wide range of scientific disciplines.
-
C.
VGN
VGN (Verkehrsverbund Großraum Nürnberg) is the public transport association that coordinates and manages integrated ticketing and services across the greater Nuremberg metropolitan area in Germany.
-
D.
VRG
VRG is the ICAO airline designator for Varig, the former Brazilian flag carrier and one of Latin America's historically significant airlines.
-
E.
VG AS
VG AS is a Norwegian media company best known for publishing Verdens Gang (VG), one of Norway’s largest and most influential newspapers and news websites.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: VVGL Triple: [Gia Lâm Airport, hasICAOCode, VVGL]
Generated description
VVGL is the ICAO airport code assigned to Gia Lâm Airport in Hanoi, Vietnam.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: VVGL Target entity description: VVGL is the ICAO airport code assigned to Gia Lâm Airport in Hanoi, Vietnam.
-
A.
VGF
VGF is the municipal public transport operator responsible for running Frankfurt am Main’s urban transit network, including its U-Bahn and tram services.
-
B.
WGL
WGL is the common abbreviation for the Leibniz Association, a major German network of non-university research institutes spanning a wide range of scientific disciplines.
-
C.
VGN
VGN (Verkehrsverbund Großraum Nürnberg) is the public transport association that coordinates and manages integrated ticketing and services across the greater Nuremberg metropolitan area in Germany.
-
D.
VRG
VRG is the ICAO airline designator for Varig, the former Brazilian flag carrier and one of Latin America's historically significant airlines.
-
E.
VG AS
VG AS is a Norwegian media company best known for publishing Verdens Gang (VG), one of Norway’s largest and most influential newspapers and news websites.
- F. None of above. chosen
Provenance (5 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_69ca848793ec8190a93a12383a754dc0 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9ad650a4819096258665bc3f410b |
completed | April 1, 2026, 10:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1797386d88190bc1d9309ecc1b4fb |
completed | April 4, 2026, 8:49 p.m. |
| NEDg | Description generation | batch_69d17a0d603881908066d61fff1d2fda |
completed | April 4, 2026, 8:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d17a769b608190b49ad82b35cf1b44 |
completed | April 4, 2026, 8:54 p.m. |
Created at: March 30, 2026, 8:10 p.m.