Triple
T2744107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Struma |
E60823
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Serres |
E104307
|
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: Serres | Statement: [Struma, passesNear, Serres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serres Context triple: [Struma, passesNear, Serres]
-
A.
Serres
chosen
Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural center.
-
B.
San Javier
San Javier is a Chilean town known for its agricultural activity and wine production in the Maule Region.
-
C.
San Fernando
San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
-
D.
San Fernando
San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
-
E.
San Fernando
San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
- 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_69ab4b79846081909096725374d65ce9 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb32ef74819096ae399d16d4f31d |
completed | March 7, 2026, 8 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbcebe788190aa2b40158b64b7b2 |
completed | March 10, 2026, 6:35 a.m. |
Created at: March 6, 2026, 9:56 p.m.