Triple
T4937607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dumna Airport |
E110849
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Dumna
Dumna is a locality in India known for its proximity to Dumna Airport and its role as a regional transport and residential area.
|
E481687
|
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: Dumna | Statement: [Dumna Airport, near, Dumna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dumna Context triple: [Dumna Airport, near, Dumna]
-
A.
Dravinja
Dravinja is a river in northeastern Slovenia that flows through the Dravinja Valley before joining the Drava River.
-
B.
Rivodutri
Rivodutri is a small Italian municipality in the Lazio region, known for its historic village setting amid the natural landscapes of the Province of Rieti.
-
C.
Dijla
Dijla is the Kurdish name for the Tigris River, one of the major rivers of Western Asia flowing through Turkey, Syria, and Iraq.
-
D.
Moura
Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
-
E.
Moura
Moura is a small coal-mining town in Central Queensland, Australia, known for its agricultural activities and history of mining disasters.
- 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: Dumna Triple: [Dumna Airport, near, Dumna]
Generated description
Dumna is a locality in India known for its proximity to Dumna Airport and its role as a regional transport and residential area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dumna Target entity description: Dumna is a locality in India known for its proximity to Dumna Airport and its role as a regional transport and residential area.
-
A.
Dravinja
Dravinja is a river in northeastern Slovenia that flows through the Dravinja Valley before joining the Drava River.
-
B.
Rivodutri
Rivodutri is a small Italian municipality in the Lazio region, known for its historic village setting amid the natural landscapes of the Province of Rieti.
-
C.
Dijla
Dijla is the Kurdish name for the Tigris River, one of the major rivers of Western Asia flowing through Turkey, Syria, and Iraq.
-
D.
Moura
Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
-
E.
Moura
Moura is a small coal-mining town in Central Queensland, Australia, known for its agricultural activities and history of mining disasters.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70872270819080769dad972681ef |
completed | March 20, 2026, 4:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be77bcb8d881908d393223bdea145a |
completed | March 21, 2026, 10:49 a.m. |
| NEDg | Description generation | batch_69be78747f3881908afe9cf276216bc5 |
completed | March 21, 2026, 10:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be78d03aec81909a2470306ba90cb0 |
completed | March 21, 2026, 10:54 a.m. |
Created at: March 20, 2026, 1:31 p.m.