Triple
T8533633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sainj Valley |
E202017
|
entity |
| Predicate | mainAccessTown |
P22318
|
FINISHED |
| Object | Sainj |
E192554
|
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: Sainj | Statement: [Sainj Valley, mainAccessTown, Sainj]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sainj Context triple: [Sainj Valley, mainAccessTown, Sainj]
-
A.
Sainj
chosen
Sainj is a small town in Himachal Pradesh, India, known as a primary access point to the Great Himalayan National Park and the scenic Sainj Valley.
-
B.
Sairang
Sairang is a small town in the Indian state of Mizoram, known for its scenic riverside setting and role as a transport and trading hub near the state capital Aizawl.
-
C.
Sangan
Sangan is a town located in Pakistan’s Balochistan province within the Sibi District.
-
D.
Nai Sarak
Nai Sarak is a bustling commercial street in Old Delhi known for its dense concentration of bookshops, stationery stores, and educational supply outlets.
-
E.
Sakia
Sakia is a prominent cultural center and arts venue in Cairo, Egypt, known for hosting concerts, exhibitions, and a wide range of cultural events.
- 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_69ca832355b08190b8b6a4ab4a4a3554 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe678fe448190a50c6b0d149b081f |
completed | March 31, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6d7aa3a881909bfdd3536dbc4ec7 |
completed | April 2, 2026, 1:22 p.m. |
Created at: March 30, 2026, 6:17 p.m.