Triple
T12049856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lohit River |
E286886
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Tezu |
E215182
|
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: Tezu | Statement: [Lohit River, passesNear, Tezu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tezu Context triple: [Lohit River, passesNear, Tezu]
-
A.
Tezu
chosen
Tezu is a prominent town in northeastern India known as an important administrative and cultural center in the Lohit district of Arunachal Pradesh.
-
B.
Matoury
Matoury is a commune in French Guiana located near Cayenne, known for its role as a suburban and economic hub that includes part of the area surrounding the Guiana Space Centre.
-
C.
Kashiwa
Kashiwa is a city in Chiba Prefecture, Japan, known as a residential and commercial hub within the Greater Tokyo metropolitan area.
-
D.
Kashima
Kashima is a city in Japan’s Kyushu region known for its traditional sake breweries and scenic coastal and rural landscapes.
-
E.
Yomi
Yomi is the shadowy land of the dead in Japanese mythology, often depicted as a gloomy underworld where the deceased reside.
- 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d904227958819084dbd5eb2566c735 |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49dd140a48190844f64c228e6367a |
completed | May 1, 2026, 12:34 p.m. |
Created at: April 8, 2026, 9:47 p.m.