Triple
T8079368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marinduque |
E188574
|
entity |
| Predicate | airportLocatedIn |
P2903
|
FINISHED |
| Object | Gasan |
E710465
|
NE 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: Gasan | Statement: [Marinduque, airportLocatedIn, Gasan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gasan Context triple: [Marinduque, airportLocatedIn, Gasan]
-
A.
Gasan
chosen
Gasan is a coastal municipality on the island province of Marinduque in the Philippines, known for its beaches, cultural festivals, and historic churches.
-
B.
Khashuri
Khashuri is a town in central Georgia that serves as an important regional transport hub and gateway between eastern and western parts of the country.
-
C.
Kadiria
Kadiria is a town and commune located within Bouira Province in northern Algeria.
-
D.
Sultangazi
Sultangazi is a densely populated urban district on the European side of Istanbul, Turkey, known for its diverse working-class communities and rapid development.
-
E.
Touqan
Touqan is a family name of notable Palestinian origin, associated with prominent poets, politicians, and intellectuals in the Arab world.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82b662e88190b9323daab8c28a21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb40a3f01c819096a2c9d5d5199fe6 |
completed | March 31, 2026, 3:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc93eddef48190b5f499a5b52428c8 |
completed | April 1, 2026, 3:41 a.m. |
Created at: March 30, 2026, 5:28 p.m.