Triple
T1000256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sima Samar |
E21586
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sima |
E21586
|
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: Sima | Statement: [Sima Samar, givenName, Sima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sima Context triple: [Sima Samar, givenName, Sima]
-
A.
Sima Samar
chosen
Sima Samar is an Afghan physician and human rights advocate renowned for her work promoting women's rights, education, and social justice in Afghanistan.
-
B.
Pei
Pei is the family name of I. M. Pei, the renowned Chinese-American architect known for designing landmarks such as the Louvre Pyramid in Paris.
-
C.
Changling
Changling is the largest and best-preserved mausoleum within Beijing’s Ming Tombs complex, built for the Yongle Emperor and his empress.
-
D.
Shimsha
Shimsha is a river in southern India that flows through Karnataka and is known for its waterfalls and contribution to the Kaveri river system.
-
E.
Shi
Shi is a Chinese surname historically associated with members of the Jewish community in Kaifeng, China.
- 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_69a493c476b48190b41fc5e793171cc6 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4fb18b88190ae2d620aaaff4f90 |
completed | March 1, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac428ba1d88190bd2fd9d3de699291 |
completed | March 7, 2026, 3:21 p.m. |
Created at: March 1, 2026, 7:41 p.m.