Triple
T18894905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Protractor Series |
E462187
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Firuzabad |
—
|
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: Firuzabad | Statement: [Protractor Series, notableWork, Firuzabad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Firuzabad Context triple: [Protractor Series, notableWork, Firuzabad]
-
A.
Firuzabad
chosen
Firuzabad is a historic city in Iran renowned for its Sassanian-era archaeological sites and distinctive circular urban plan.
-
B.
Meherabad
Meherabad is a spiritual retreat and pilgrimage center in Maharashtra, India, best known as the ashram and tomb-shrine of Indian spiritual master Meher Baba.
-
C.
Nurabad
Nurabad is a city in western Iran that serves as a local urban center within Lorestan Province.
-
D.
Ardestan
Ardestan is an ancient city in central Iran known for its historic architecture, including notable mosques and traditional urban fabric.
-
E.
Piranshahr
Piranshahr is a predominantly Kurdish city in northwestern Iran known for its mountainous surroundings and role as a regional commercial center.
- 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c47dfa8c8190940858a010129d13 |
completed | April 20, 2026, 6:15 a.m. |
Created at: April 10, 2026, 11:58 a.m.