Triple
T10888193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anindya Bose |
E257104
|
entity |
| Predicate | formerMemberOf |
P1168
|
FINISHED |
| Object | Shahar |
E892348
|
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: Shahar | Statement: [Anindya Bose, formerMemberOf, Shahar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shahar Context triple: [Anindya Bose, formerMemberOf, Shahar]
-
A.
Shahar
chosen
Shahar is an organization or group that includes Anindya Bose among its members.
-
B.
Shahrak
Shahrak is a town located in Afghanistan’s central-western Ghor Province.
-
C.
Sadras
Sadras is a historic coastal town in Tamil Nadu, India, known for its Dutch-era fort and role as a former trading port on the Coromandel Coast.
-
D.
Bais City
Bais City is a component city in Negros Oriental in the Central Visayas region of the Philippines, known for its sugar industry and dolphin- and whale-watching tourism.
-
E.
Badrashin city
Badrashin city is an urban center in Giza Governorate, Egypt, known for its proximity to several important archaeological and historical sites from ancient Egypt.
- 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_69d6aa848804819081b2713ca0bedf06 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75201e6888190a2bc41a17784eec3 |
completed | April 9, 2026, 7:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2169ea02c8190addf125ec5adafe8 |
completed | April 17, 2026, 11:16 a.m. |
Created at: April 8, 2026, 9:21 p.m.