Triple
T19387659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Petrified Wood Gas Station |
E484976
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Lamar |
—
|
NE NERFINISHED |
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: Lamar | Statement: [Petrified Wood Gas Station, city, Lamar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lamar Context triple: [Petrified Wood Gas Station, city, Lamar]
-
A.
Lamar
Lamar is a surname most notably associated with Mirabeau B. Lamar, the second president of the Republic of Texas.
-
B.
Lamar
chosen
Lamar is a small city in southeastern Colorado that serves as an agricultural and transportation hub for the surrounding rural region.
-
C.
Lamar
Lamar is a masculine given name of Old French and Old German origin, commonly used in the United States.
-
D.
Houstoun
Houstoun is a surname of Scottish origin associated with various notable individuals in politics, law, and public life.
-
E.
Gatlin
Gatlin is a surname of English origin borne by various notable individuals across fields such as music, sports, and politics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61b418f148190972b7b46038bc744 |
completed | April 20, 2026, 12:25 p.m. |
Created at: April 10, 2026, 1:36 p.m.