Triple
T6716387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orontes River |
E153279
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Hama |
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 | Statement: [Orontes River, flowsThrough, Hama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hama Context triple: [Orontes River, flowsThrough, Hama]
-
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.
Shama
Shama is a coastal town in Ghana known historically as a fishing community and trading post along the Gulf of Guinea.
-
C.
Hamey
Hamey is a diminutive or affectionate nickname derived from the given name Hamish.
-
D.
Tama
Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
-
E.
Tama
Tama was a Japanese light cruiser of the Imperial Japanese Navy that served in World War II before being sunk during the Battle off Cape Engaño in 1944.
- 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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d125db3c8190aad28919226a16da |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c700993128819081614ccfa68d7320 |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:07 p.m.