Triple
T22335783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Shahnaz Pahlavi |
E552143
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Tehran |
—
|
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: Tehran | Statement: [Princess Shahnaz Pahlavi, placeOfBirth, Tehran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tehran Context triple: [Princess Shahnaz Pahlavi, placeOfBirth, Tehran]
-
A.
Tehran
chosen
Tehran is the capital and largest city of Iran, serving as the country's political, economic, and cultural center.
-
B.
Shahr-e Rey
Shahr-e Rey is an ancient city now absorbed into the metropolitan area of Tehran, Iran, known for its long history as a major political and cultural center in the region.
-
C.
Tehrani
Tehrani is a Persian surname most notably associated with Iranian actress Hedieh Tehrani.
-
D.
Teheran-ro
Teheran-ro is a major business and technology corridor in Seoul, South Korea, known for its concentration of corporate headquarters, startups, and high-rise office buildings.
-
E.
Isfahan
Isfahan is a historic Iranian city renowned for its Safavid-era architecture, grand mosques, and role as a major political and cultural center in early modern Persia.
- 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_69e11e494eec81909c4d2d51f69499d9 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1577f2f208190ac6270ac4581fa15 |
completed | April 29, 2026, 12:57 a.m. |
Created at: April 16, 2026, 8:43 p.m.