Triple
T37336921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kashid Beach |
E926916
|
entity |
| Predicate | distanceFromAlibaug |
P205840
|
FINISHED |
| Object | about 30 km by road |
—
|
LITERAL 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: about 30 km by road | Statement: [Kashid Beach, distanceFromAlibaug, about 30 km by road]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAlibaug Context triple: [Kashid Beach, distanceFromAlibaug, about 30 km by road]
-
A.
distanceFromOlean (miles)
Indicates the number of miles separating a given location from Olean.
-
B.
distanceToSyracuse
Indicates the spatial distance between a given entity’s location and the location of Syracuse.
-
C.
distanceToAlbanyNewYork
Indicates the spatial distance between a given location and Albany, New York.
-
D.
distanceFromOlean (kilometers)
Indicates the straight-line or travel distance, measured in kilometers, between a given location and the reference point of Olean.
-
E.
distanceToBuffalo
Indicates the measured spatial distance between a given entity and the location of Buffalo.
- F. None of above. chosen
Provenance (4 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_69f76eb4e8a881908bd40da28f36fc7e |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a13a1308190a202df66f4781855 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c842b2c819082f1d2db995ac2eb |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:16 p.m.