Triple
T22734549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oruzgan |
E562228
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Tarinkot |
—
|
NE NERFINISHED |
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: Tarinkot | Statement: [Oruzgan, hasCity, Tarinkot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarinkot Context triple: [Oruzgan, hasCity, Tarinkot]
-
A.
Tarinkot
chosen
Tarinkot is a small city in central Afghanistan that serves as the administrative and economic hub of Oruzgan Province.
-
B.
Timket
Timket is the Ethiopian Orthodox celebration of Epiphany, marked by vibrant religious processions, blessings of water, and traditional cultural attire and festivities.
-
C.
Pazarlar
Pazarlar is a small town and district in western Turkey known for its rural character and location within Kütahya Province.
-
D.
Bazmark
Bazmark is an Australian film production company founded by director Baz Luhrmann, known for visually lavish and stylistically distinctive movies such as "Moulin Rouge!" and "The Great Gatsby."
-
E.
Tiendesitas
Tiendesitas is a popular shopping and lifestyle complex in Pasig, Metro Manila, known for its Filipino-themed architecture, handicrafts, food, and live entertainment.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24550859c81908727d91efc3a81b4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1796f030881908e141564d442bd1b |
completed | April 29, 2026, 3:22 a.m. |
Created at: April 17, 2026, 3:22 p.m.