Triple
T2458888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arecibo Observatory |
E54484
|
entity |
| Predicate | numberOfReceivers |
P40621
|
FINISHED |
| Object | multiple radio receivers |
—
|
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: multiple radio receivers | Statement: [Arecibo Observatory, numberOfReceivers, multiple radio receivers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfReceivers Context triple: [Arecibo Observatory, numberOfReceivers, multiple radio receivers]
-
A.
totalRecipients
Indicates the total number of distinct entities that receive something in the context of the described relationship or action.
-
B.
numberOfHosts
Indicates the total count of distinct hosts associated with or involved in a given entity or event.
-
C.
numberOfAntennas
Indicates the quantity of antennas that an entity possesses or is associated with.
-
D.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
E.
numberOfSubscribers
Indicates the total count of subscribers associated with a given entity.
- 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_69ab49dee84c819096b50a0049c347ac |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd49c5aa081909ab4f726a458b77f |
completed | March 7, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69abd0b199488190aa381b36593ae1ac |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd49b5d2481908817aeb171e2bd61 |
completed | March 7, 2026, 7:32 a.m. |
Created at: March 6, 2026, 9:44 p.m.