Triple
T8837100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alor archipelago |
E210291
|
entity |
| Predicate | ocean |
P1778
|
FINISHED |
| Object | Saw Sea |
E124161
|
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: Saw Sea | Statement: [Alor archipelago, ocean, Saw Sea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saw Sea Context triple: [Alor archipelago, ocean, Saw Sea]
-
A.
Saw Sea
chosen
The Saw Sea is a body of water located off the western part of Timor in Southeast Asia.
-
B.
Saaho
Saaho is a 2019 Indian action thriller film known for its high-budget production, elaborate action sequences, and starring Prabhas in the lead role.
-
C.
Swordfish
Swordfish is a 2001 action thriller film known for its high-tech heist plot, stylized action sequences, and performances by John Travolta, Hugh Jackman, and Halle Berry.
-
D.
Die Haie
Die Haie is the popular nickname of the Kölner Haie, a professional ice hockey team based in Cologne, Germany.
-
E.
Shark Alley
Shark Alley is a popular National Aquarium exhibit featuring a large collection of sharks and other marine predators in an immersive, walk-through viewing environment.
- 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_69ca8388549c819095fd94eadefbb007 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc606adde08190825dbdabd199c025 |
completed | April 1, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf898a478c81908f138a78f331b87d |
completed | April 3, 2026, 9:34 a.m. |
Created at: March 30, 2026, 6:48 p.m.