Triple
T15177548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beaver Club |
E362649
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Peter Pond
Peter Pond was an 18th-century American fur trader, explorer, and cartographer who played a key role in opening up the Canadian Northwest for the fur trade.
|
E1141136
|
NE FINISHED |
How this triple was built (4 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: Peter Pond | Statement: [Beaver Club, hasMember, Peter Pond]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Pond Context triple: [Beaver Club, hasMember, Peter Pond]
-
A.
Paul Millspaugh
Paul Millspaugh is a film editor known for his work on the romantic comedy "Two Can Play That Game."
-
B.
Peter Veeder
Peter Veeder was an individual significant enough in regional history or exploration to have Mount Veeder named in his honor.
-
C.
Nils Pierson
Nils Pierson is an individual notable enough to be recognized as a bearer of the surname Pierson.
-
D.
Andrew Pyle
Andrew Pyle is a British philosopher known for his work in the philosophy of science, metaphysics, and the history of early modern philosophy.
-
E.
Peter Pau
Peter Pau is an acclaimed Hong Kong cinematographer best known internationally for his Oscar-winning work on the film "Crouching Tiger, Hidden Dragon."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Peter Pond Triple: [Beaver Club, hasMember, Peter Pond]
Generated description
Peter Pond was an 18th-century American fur trader, explorer, and cartographer who played a key role in opening up the Canadian Northwest for the fur trade.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peter Pond Target entity description: Peter Pond was an 18th-century American fur trader, explorer, and cartographer who played a key role in opening up the Canadian Northwest for the fur trade.
-
A.
Paul Millspaugh
Paul Millspaugh is a film editor known for his work on the romantic comedy "Two Can Play That Game."
-
B.
Peter Veeder
Peter Veeder was an individual significant enough in regional history or exploration to have Mount Veeder named in his honor.
-
C.
Nils Pierson
Nils Pierson is an individual notable enough to be recognized as a bearer of the surname Pierson.
-
D.
Andrew Pyle
Andrew Pyle is a British philosopher known for his work in the philosophy of science, metaphysics, and the history of early modern philosophy.
-
E.
Peter Pau
Peter Pau is an acclaimed Hong Kong cinematographer best known internationally for his Oscar-winning work on the film "Crouching Tiger, Hidden Dragon."
- F. None of above. chosen
Provenance (5 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00663b4148190b647592eda315d1d |
completed | April 15, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec89061548190b0b10da00b8d937e |
completed | May 9, 2026, 5:39 a.m. |
| NEDg | Description generation | batch_69fec91a2d708190bcc67793c46b2a61 |
completed | May 9, 2026, 5:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69feca0d38088190910dbf4f2538a9d4 |
completed | May 9, 2026, 5:45 a.m. |
Created at: April 10, 2026, 3:09 a.m.