Triple
T15332462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankston, Texas |
E366568
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Lake Palestine |
E370176
|
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: Lake Palestine | Statement: [Frankston, Texas, locatedNear, Lake Palestine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lake Palestine Context triple: [Frankston, Texas, locatedNear, Lake Palestine]
-
A.
Lake Palestine
chosen
Lake Palestine is a large reservoir in East Texas popular for fishing, boating, and lakeside recreation.
-
B.
Lake Gilead
Lake Gilead is a small reservoir in Putnam County, New York, that serves as part of New York City's Croton water supply system.
-
C.
Lake Jordan
Lake Jordan is a man-made reservoir in central Alabama known for recreational boating, fishing, and lakeside residential communities.
-
D.
Lake Sai
Lake Sai is one of the Fuji Five Lakes in Yamanashi Prefecture, Japan, known for its scenic views of Mount Fuji and tranquil natural surroundings.
-
E.
Lake Zoar
Lake Zoar is a man-made reservoir in western Connecticut popular for boating, fishing, and other recreational activities.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e0268608190947a58f559a67717 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01ecb904819082454622dcd77556 |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:17 a.m.