Triple
T3405104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hama |
E71751
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object | Hama norias |
E71751
|
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: Hama norias | Statement: [Hama, hasLandmark, Hama norias]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hama norias Context triple: [Hama, hasLandmark, Hama norias]
-
A.
Hama
chosen
Hama is a major city in west-central Syria, historically known for its ancient waterwheels (norias) on the Orontes River and its role as an important agricultural and industrial center.
-
B.
Daikanransha Ferris Wheel
The Daikanransha Ferris Wheel is a large, iconic observation wheel in Tokyo’s Odaiba district, offering panoramic views of the city skyline and Tokyo Bay.
-
C.
Kamitsumaki
Kamitsumaki is the first volume of the ancient Japanese chronicle Kojiki, focusing on Shinto creation myths and the age of the gods.
-
D.
Ten-Eyed Bridge
Ten-Eyed Bridge is a historic multi-arched stone bridge spanning the Tigris River near Diyarbakır in southeastern Turkey.
-
E.
Nanko-kita
Nanko-kita is a district within Osaka’s artificial Sakishima Island area, known for its waterfront urban development and commercial facilities.
- 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_69ad85aac4808190a092c9cc8911f584 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb8eaa41c819095a4d51aec074649 |
completed | March 8, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34bd69f388190981da6454dfd4fb1 |
completed | March 12, 2026, 11:27 p.m. |
Created at: March 8, 2026, 3:15 p.m.