Triple
T16408092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dunedin Airport |
E398487
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Momona
Momona is a small rural township on the Taieri Plains near Dunedin in New Zealand’s South Island.
|
E1212366
|
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: Momona | Statement: [Dunedin Airport, locatedIn, Momona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Momona Context triple: [Dunedin Airport, locatedIn, Momona]
-
A.
Ménaka
Ménaka is a town in eastern Mali that serves as an important administrative and trading center in the Sahel region.
-
B.
Malalai
Malalai is an Afghan activist and former politician internationally recognized for her outspoken criticism of warlords, the Taliban, and foreign occupation in Afghanistan.
-
C.
Katisha
Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
-
D.
Mamo
Mamo is a Maltese surname most notably borne by Sir Anthony Mamo, the first President of Malta.
-
E.
Anuta
Anuta is a small, remote Polynesian outlier island in the Solomon Islands known for its dense population, strong communal culture, and well-preserved traditional way of life.
- 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: Momona Triple: [Dunedin Airport, locatedIn, Momona]
Generated description
Momona is a small rural township on the Taieri Plains near Dunedin in New Zealand’s South Island.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Momona Target entity description: Momona is a small rural township on the Taieri Plains near Dunedin in New Zealand’s South Island.
-
A.
Ménaka
Ménaka is a town in eastern Mali that serves as an important administrative and trading center in the Sahel region.
-
B.
Malalai
Malalai is an Afghan activist and former politician internationally recognized for her outspoken criticism of warlords, the Taliban, and foreign occupation in Afghanistan.
-
C.
Katisha
Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
-
D.
Mamo
Mamo is a Maltese surname most notably borne by Sir Anthony Mamo, the first President of Malta.
-
E.
Anuta
Anuta is a small, remote Polynesian outlier island in the Solomon Islands known for its dense population, strong communal culture, and well-preserved traditional way of life.
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32870e44c8190aae7bc6e6022ceb7 |
completed | April 18, 2026, 6:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c64a05c8190a59e800ce2318052 |
completed | May 10, 2026, 8:05 a.m. |
| NEDg | Description generation | batch_6a003dfdd4f88190b86db12bb9c7217a |
completed | May 10, 2026, 8:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a003e953ca88190bb7c64c12a46c666 |
completed | May 10, 2026, 8:15 a.m. |
Created at: April 10, 2026, 5:09 a.m.