Triple
T6049708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daggett |
E134758
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Yermo |
E136890
|
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: Yermo | Statement: [Daggett, locatedNear, Yermo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yermo Context triple: [Daggett, locatedNear, Yermo]
-
A.
Yermo
chosen
Yermo is a small unincorporated desert community in San Bernardino County, California, located along Interstate 15 near Barstow.
-
B.
Bozkır
Bozkır is a rural district and town in Turkey known for its mountainous terrain and agricultural activities, located within Konya Province in Central Anatolia.
-
C.
Yadamah
Yadamah is a town in southwestern Saudi Arabia located within the Najran Region near the border with Yemen.
-
D.
Krakhuna
Krakhuna is a Georgian white grape variety from the Imereti region, known for producing aromatic, full-bodied wines with pronounced acidity.
-
E.
Daura
Daura is a historic town in Katsina State, northern Nigeria, regarded as a cultural center of the Hausa people and known as the hometown of former president Muhammadu Buhari.
- 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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056f4f84c8190a7e2416d81b3554c |
completed | March 22, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c113a65164819090883dbad3be5026 |
completed | March 23, 2026, 10:19 a.m. |
Created at: March 22, 2026, 4:09 p.m.