Triple
T11192697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gascoyne region |
E264839
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Monkey Mia |
E623703
|
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: Monkey Mia | Statement: [Gascoyne region, containsTown, Monkey Mia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monkey Mia Context triple: [Gascoyne region, containsTown, Monkey Mia]
-
A.
Monkey Mia
chosen
Monkey Mia is a remote coastal area in Western Australia famous for its calm turquoise waters and wild bottlenose dolphins that regularly visit the shore.
-
B.
Monkey 47
Monkey 47 is a premium German craft gin renowned for its complex flavor profile derived from 47 botanicals and its origin in the Black Forest.
-
C.
Monkey Me
"Monkey Me" is a 2012 electropop studio album by French singer-songwriter Mylène Farmer, known for its melancholic themes and atmospheric production.
-
D.
Toto
Toto is the nickname of Italian former footballer Salvatore Schillaci, famed for his standout goal-scoring performance at the 1990 FIFA World Cup.
-
E.
Toto
Toto is a local government area in Nasarawa State, Nigeria, known for its predominantly rural communities and agrarian-based economy.
- 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8be025481909d311b587418dfb2 |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e483f8ecf4819086f0bab3ca9ddcb4 |
completed | April 19, 2026, 7:27 a.m. |
Created at: April 8, 2026, 9:29 p.m.