Triple
T5443721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sambandar |
E122196
|
entity |
| Predicate | contemporaryOf |
P6401
|
FINISHED |
| Object | Appar |
E118518
|
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: Appar | Statement: [Sambandar, contemporaryOf, Appar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Appar Context triple: [Sambandar, contemporaryOf, Appar]
-
A.
Appar
chosen
Appar was a prominent 7th-century Tamil Shaivite saint and poet whose devotional hymns greatly shaped the Bhakti movement in South India.
-
B.
Aparan
Aparan is a small town in Armenia’s Aragatsotn Province, known for its proximity to Mount Aragats and its historic churches and monuments.
-
C.
Anput
Anput is an ancient Egyptian goddess associated with funerary rites and protection, often depicted as a female counterpart to the jackal-headed god Anubis.
-
D.
Anytos
Anytos is a figure from Greek mythology known primarily as a Titan or divine guardian associated with the Arcadian goddess Despoina.
-
E.
Autoport
Autoport is a specialized automotive terminal within the Port of Boston used for handling, storing, and processing imported and exported vehicles.
- 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_69bd4640f52c81909e653ec361f66d76 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd91ccdd648190940c04781c4222ec |
completed | March 20, 2026, 6:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf4881d6308190a4e7cb784eb520c9 |
completed | March 22, 2026, 1:40 a.m. |
Created at: March 20, 2026, 2:07 p.m.