Triple
T4606497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tausug |
E100449
|
entity |
| Predicate | associatedWithProvince |
P20236
|
FINISHED |
| Object | Sulu |
E458383
|
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: Sulu | Statement: [Tausug, associatedWithProvince, Sulu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sulu Context triple: [Tausug, associatedWithProvince, Sulu]
-
A.
Sulu
chosen
Sulu is an island province in the southern Philippines known for its predominantly Muslim population, rich Tausug culture, and historical role as the center of the Sultanate of Sulu.
-
B.
Phusro
Phusro is a coal-mining and industrial town in the Bokaro district of Jharkhand, India.
-
C.
Luyana
Luyana is a Bantu language of southwestern Africa that historically served as a prestige and source language for the development of the Lozi language.
-
D.
Shapuri
Shapuri is a regional dialect of the Lahnda (Western Punjabi) language spoken in parts of Pakistan’s Punjab region.
-
E.
Taif
Taif is a city in western Saudi Arabia known for its cool climate, rose cultivation, and historical significance as a summer resort and cultural center.
- 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_69bd43cce1e08190a07d53af6a9b6c24 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd599c50d08190ab226cd0691e29f9 |
completed | March 20, 2026, 2:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be034e509c8190b2da7e2eaf2acb48 |
completed | March 21, 2026, 2:32 a.m. |
Created at: March 20, 2026, 1:12 p.m.