Triple
T20066679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater Tehran |
E499624
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Fardis |
—
|
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: Fardis | Statement: [Greater Tehran, contains, Fardis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fardis Context triple: [Greater Tehran, contains, Fardis]
-
A.
Fardis
chosen
Fardis is a city in Iran that serves as an urban center within the country's Alborz Province.
-
B.
Farhual
Farhual is a regional dialect of the Hakha Chin language spoken by Chin communities in parts of Myanmar and neighboring areas.
-
C.
Fad‘an
Fad‘an is a subtribe of the large and historically influential Arab tribal confederation of Anizah.
-
D.
Fayiz
Fayiz is a masculine given name of Arabic origin, commonly used as a variant transliteration of the name Fayez.
-
E.
Muladis
Muladis were Muslims in medieval Iberia who were originally local Christians that had converted to Islam, often blending Arab-Islamic and Iberian cultural elements.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66379f2cc81908f13a7b216878f12 |
completed | April 20, 2026, 5:33 p.m. |
Created at: April 11, 2026, 3:39 p.m.